Système de solveurs

dispatchatlas.solve possède les métadonnées de solveurs, la sélection, la construction d'ordonnancements, la réparation, les garde-fous de performance, les familles de référence, les adaptateurs optionnels, et la famille NDSO.

Le paquet importe dispatchatlas.core uniquement. Les tests d'intégration de test de benchmarks vivent dans tests/solve/ afin que le paquet d'exécution ne dépende pas de générateurs de benchmarks concrets.

Exemple exécutable : examples/compare_solvers.py ordonnance une cohorte de solveurs en parallèle et l'ancre avec un optimum exact prouvé.

Registre

SolverRegistry stocke des fabriques de solveurs sans état avec des métadonnées riches :

des étiquettes de capacité comme capacity-aware, precedence-aware, repair, local-search, et ndso
des objectifs pris-en-charge comme makespan, energy, et cost
des contraintes déclarées exprimées via des étiquettes de capacité
des critères d'arrêt par défaut
un comportement de rejeu déterministe ou stochastique-ensemencé
un encodage de solution (permutation, mapping, assignment, ou native)
des déclarations de dépendance optionnelle et la divulgation de backend commercial
une visibilité de niveau-de-preuve pour les exports par étapes
une citation canonique, ou une justification explicite de citation-non-applicable

Chaque famille de solveur nommée porte une citation qui échoue en position fermée : une famille avec une origine séminale canonique qui omet sa référence ne peut être construite, et une famille sans origine canonique unique enregistre la raison plutôt que d'en fabriquer une.

from dispatchatlas.solve import SolverCapability, default_solver_registry
 
registry = default_solver_registry()
metaheuristics = registry.select(
    required_capabilities=(SolverCapability.METAHEURISTIC,),
    objective="makespan",
)

Catalogue du registre

Chaque solveur du registre, dans une seule table triable et cherchable. La table et ses totaux sont générés à partir de default_solver_registry(), de sorte que les comptes ci-dessous sont dénombrables depuis les lignes elles-mêmes. Un graphique de couverture-des-capacités au-dessus de la table résume combien de solveurs du registre déclarent chaque capacité déclarée.

Generated from the solver registry: 87 solvers across 6 groups — constructive (5), dispatching (14), exact (7), learning (11), metaheuristic (47), ndso (3).

How many of the 87 registry solvers advertise each declared capability — a metadata-only view of the registry’s breadth (a constructive baseline through to many-objective and native solvers). Hover or focus a bar to read its count.

Search solversGroupGroup by

Showing 87 of 87 solvers.

Solver registry — 87 rows, build-inlined from the public registry bundle.
		Supported objectives	Notes
`adpso`	metaheuristic	makespan, energy, cost	Inspect caveats success-rate feedback is noisy on small swarms competitor adpso currency anchor Sensors (MDPI), 2022 encoding adapter random-key evidence class runnable-baseline mechanism swarm search with success-adaptive descending inertia strengths inertia tracks search progress instead of a fixed ramp venue tier indexed-peer-reviewed
`age-moea-ii`	metaheuristic	makespan, lateness, fairness, energy, cost, carbon	Inspect caveats geometry estimate is noisy on tiny first fronts competitor age-moea-ii evidence class runnable-baseline geometry Newton-Raphson curvature fit of the sum(x^p)=1 surface mechanism adaptive front-geometry estimation with geodesic survival strengths spread faithful to convex, linear, and concave fronts
`ant-colony`	metaheuristic	makespan, energy, cost	Inspect mechanism rate-gated adjacent swaps of the incumbent order metaheuristic ant-colony realization no pheromone trail matrix or cooperating colony is implemented; the citation marks lineage, not mechanism fidelity scheduling contract permutation-decode-to-schedule
`apparent-tardiness-cost`	dispatching	makespan, lateness, energy, cost	Inspect dispatch rule apparent-tardiness-cost priority basis highest apparent-tardiness-cost index first (unweighted, k=2)
`arithmetic-optimization`	metaheuristic	makespan, energy, cost	Inspect caveats every member is regenerated around the best vector each iteration, so the returned order is the best observed rather than a monotone incumbent discrete adaptation Abualigah, L., Diabat, A., & Abd Elaziz, M. (2021). Intelligent workflow scheduling for Big Data applications in IoT cloud computing environments. Cluster Computing, 24, 2957-2976. doi:10.1007/s10586-021-03291-7 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search whose accelerator schedule shifts from wide-scatter division and multiplication moves to tight addition and subtraction moves around the best vector as the budget elapses strengths the two operator pairs give a clean exploration-to-exploitation handover governed only by the iteration ratio
`artificial-bee-colony`	metaheuristic	makespan, energy, cost	Inspect caveats the per-component neighbor move explores slowly on long priority vectors, so many iterations may be needed at large task counts discrete adaptation Pan, Q.-K., Tasgetiren, M. F., Suganthan, P. N., & Chua, T. J. (2011). A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences, 181(12), 2455-2468. doi:10.1016/j.ins.2009.12.025 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search with three phases per iteration: a greedy neighbor perturbation of every member toward a random peer, a fitness-weighted resample that perturbs promising members again, and a re-initialization of any member that fails to improve for a set number of trials strengths the fitness-weighted resample concentrates effort on promising members while the trial-limit re-initialization sustains diversity
`artificial-fish-swarm`	metaheuristic	makespan, energy, cost	Inspect caveats each behaviour re-scores candidate positions, so an iteration costs several schedule evaluations per member discrete adaptation Ibadi, A. M., & Rahman, R. A. (2024). Modified artificial fish swarm algorithm to solve unrelated parallel machine scheduling problem under fuzzy environment. AIMS Mathematics, 9(1), 1679-1705. doi:10.3934/math.20241679 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search where each member adopts the better of a swarm move toward nearby members' centre and a follow move toward the best nearby member, each falling back to a local random probe when its move condition fails strengths the crowding test spreads members across several basins rather than collapsing them onto one centre
`beam-search`	constructive	makespan, energy, cost	Inspect beam width 3 mechanism breadth-bounded constructive search priority basis best topological completion score
`branch-and-bound`	exact	makespan, energy, cost	Inspect commercial false dependency ortools dependency extra exact dependency purpose branch-and-bound / branch-and-cut adapter dependency requirement optional exact method branch-and-bound license note OR-Tools is Apache-2.0 licensed; no commercial terms. optimality verification solves-to-optimal-verified
`ccgp`	metaheuristic	makespan, energy, cost	Inspect caveats rule evaluation cost grows with population and graph size competitor ccgp currency anchor Computers & Operations Research (Elsevier), 2024 evidence class runnable-baseline mechanism cooperative coevolution of priority-rule trees strengths learns instance-feature priority rules instead of orders venue tier top-tier-operations-research
`clpso`	metaheuristic	makespan, energy, cost	Inspect decode rule random-key encoded solver clpso encoding adapter random-key repair policy topological-normalize transfer identity
`cma-es`	metaheuristic	makespan, energy, cost	Inspect caveats the random-key encoding is a documented adaptation of the continuous method to the discrete order space encoding tasks sequenced by ascending random key, repaired to precedence feasibility evidence class runnable-baseline mechanism covariance-matrix-adaptation evolution strategy over a random-key encoding of the task order recombination weighted mean of the best mu offspring with rank-one and rank-mu covariance updates step size control cumulative step-size adaptation with the h_sigma stall guard strengths adapts an anisotropic search distribution to the objective landscape without hand-tuned scaling
`cpop`	dispatching	makespan, energy, cost	Inspect mechanism combined upward-and-downward rank list scheduling priority basis upward plus downward rank; critical tasks first rank basis combined-rank realization rank-ordered dispatch; resource and mode binding by the serial constructor's earliest-finish rule, a documented variant of the published critical-path-processor pinning
`critical-path-tabu`	metaheuristic	makespan, energy, cost	Inspect caveats path relinking between elite solutions is not realized; a documented reduction of the published method competitor critical-path-tabu evidence class runnable-baseline mechanism critical-path block-boundary tabu search relates to tabu-search: the mechanism-diversified competitor realization strengths moves concentrate on the makespan-defining chain
`cuckoo-search`	metaheuristic	makespan, energy, cost	Inspect caveats the Levy move is greedy per member, so progress depends on the incumbent and the step scale rather than a monotone population discrete adaptation Marichelvam, M. K., Prabaharan, T., & Yang, X.-S. (2014). Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Applied Soft Computing, 19, 93-101. doi:10.1016/j.asoc.2014.02.005 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search that steps every member toward the best-so-far solution by a heavy-tailed Levy displacement, keeps the step only when it improves, and re-initializes a fixed fraction of the worst members each iteration strengths the heavy-tailed step occasionally makes a large jump that escapes local optima while the discovery step sustains diversity
`d-clpso`	metaheuristic	makespan, energy, cost	Inspect decode rule spv encoded solver d-clpso encoding adapter spv repair policy topological-normalize search domain [-1.0, 1.0] transfer identity
`d-depso`	metaheuristic	makespan, energy, cost	Inspect hybrid of differential-evolution + particle-swarm scheduling contract permutation-decode-to-schedule scheduling variant d-depso
`d-lshade`	metaheuristic	makespan, energy, cost	Inspect decode rule rounding encoded solver d-lshade encoding adapter rounding repair policy topological-normalize transfer identity
`dan-dual-attention`	learning	makespan, energy, cost	Inspect documented adaptation faithful dual-attention architecture -- the operation message attention block over the precedence window and the machine message attention block over the machine-competition graph -- adapting the published amortized PPO-clip training to single-instance REINFORCE over seeded rollouts, greedy order decode, and the platform's uniform per-machine durations that collapse the (operation, machine) action to the operation order the serial decoder scores evidence class runnable-baseline learned paradigm policy-gradient dual-attention construction mechanism dual-attention network + policy-gradient PDR scheduler optional extra learning policy basis dual-attention network with an operation message attention block over the precedence window and a machine message attention block over the machine-competition graph, scoring compatible operation-machine pairs for a size-agnostic dispatching policy training single-instance REINFORCE over seeded rollouts with a terminal normalized-improvement reward and a mean-return baseline
`decima-dag-rl`	learning	makespan, energy, cost	Inspect documented adaptation faithful Decima two-level DAG message passing and REINFORCE policy gradient; the composite (node, parallelism-limit) action collapses to node selection because the platform exposes no parallelism-limit control, per-node critical-path features replace the reference executor and job-level features that have no platform counterpart, and single-instance training with a greedy decode and a terminal normalized schedule-score reward adapt the published cross-instance training and time-in-system reward to the platform's per-instance solve contract evidence class runnable-baseline learned paradigm policy-gradient GNN construction (REINFORCE) mechanism Decima two-level DAG-GNN + REINFORCE construction scheduler optional extra learning policy basis two-level DAG message passing with per-node, per-DAG, and global summaries training single-instance REINFORCE over seeded rollouts with a running-mean baseline and a terminal normalized-improvement reward
`decision-diagram-sequencing`	exact	makespan, energy, cost	Inspect commercial false exact method decision-diagram license note Native pure-Python decision-diagram branch and bound; no third-party backend and no licensing constraints. native task cap 12 optimality verification solves-to-optimal-verified
`differential-evolution`	metaheuristic	makespan, energy, cost	Inspect mechanism positional nudge toward a uniformly drawn donor order metaheuristic differential-evolution realization a discrete recombination, not the cited difference-vector arithmetic; the citation marks lineage, not mechanism fidelity scheduling contract permutation-decode-to-schedule
`earliest-deadline`	dispatching	makespan, energy, cost	Inspect dispatch rule earliest-deadline priority basis earliest declared deadline first (EDD / deadline-aware)
`earliest-finish-time`	dispatching	makespan, energy, cost	Inspect dispatch rule earliest-finish-time priority basis earliest reachable finish first (release + duration)
`earliest-start`	dispatching	makespan, energy, cost	Inspect dispatch rule input-order priority basis input order
`epso`	metaheuristic	makespan, energy, cost	Inspect caveats path gathering can over-constrain loosely coupled graphs competitor epso currency anchor Electronics (MDPI), 2023 encoding adapter random-key evidence class runnable-baseline mechanism workload-biased-initialization swarm with path gathering strengths quality-seeded swarm over a reduced particle dimension venue tier indexed-peer-reviewed
`exhaustive-enumeration`	exact	makespan, energy, cost	Inspect commercial false exact method exhaustive-enumeration license note Native pure-Python exact enumeration; no third-party backend and no licensing constraints. native task cap 8 optimality verification solves-to-optimal-verified
`firefly-algorithm`	metaheuristic	makespan, energy, cost	Inspect caveats the pairwise attraction is quadratic in the population size, and positions move unconditionally so the returned order is the best observed rather than a monotone incumbent discrete adaptation Sayadi, M. K., Hafezalkotob, A., & Naini, S. G. J. (2013). Firefly-inspired algorithm for discrete optimization problems: An application to manufacturing cell formation. Journal of Manufacturing Systems, 32(1), 78-84. doi:10.1016/j.jmsy.2012.06.004 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search attracting every priority vector toward every brighter member by a distance-decaying step plus a randomization term that shrinks over the budget strengths distance-decaying attraction lets subgroups converge on several basins before the randomization term fades
`fjsp-hgnn-drl`	learning	makespan, energy, cost	Inspect documented adaptation faithful HGNN architecture; single-instance training, greedy order decode, and uniform per-machine durations adapt the published amortized FJSP scheduler to the platform's per-instance task-order model evidence class runnable-baseline learned paradigm policy-gradient GNN construction (PPO) mechanism heterogeneous-GNN + PPO construction scheduler optional extra learning policy basis heterogeneous graph attention over operations and machines training single-instance proximal policy optimization over seeded rollouts with a terminal normalized-improvement reward
`genetic-algorithm`	metaheuristic	makespan, energy, cost	Inspect mechanism elite-pair order crossover with adjacent-swap mutation metaheuristic genetic-algorithm realization selection-crossover-mutation loop over a permutation population; faithful in kind to the cited scheme at baseline scale scheduling contract permutation-decode-to-schedule
`grasshopper-optimization`	metaheuristic	makespan, energy, cost	Inspect caveats the pairwise social forces are quadratic in the population size, and positions move unconditionally each iteration; on a Largest Order Value decode the published update adds little beyond its initial population, because it replaces each position with the target plus the force sum rather than perturbing it, and the resulting displacement peaks near a tenth of the gap between adjacent priorities -- short of the separation a rank swap needs. The shortfall is scale-free in the priority span and does not close with population size, so it is a property of the method on this encoding rather than a tuning choice discrete adaptation Naveen Durai, S. K., & Duraisamy, B. (2022). Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm for Cloud Load Balancing. Intelligent Automation & Soft Computing, 34(1), 467-483. doi:10.32604/iasc.2022.026020 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search displacing every member by the sum of pairwise attraction/repulsion forces from all others, scaled toward the best-so-far target by a comfort coefficient that decays over the budget strengths the decaying comfort coefficient shrinks the repulsion zone so the swarm settles from exploration onto the target
`gravitational-search`	metaheuristic	makespan, energy, cost	Inspect caveats the pairwise forces are quadratic in the population size, and the velocity carries momentum that can overshoot on a coarse budget discrete adaptation Choudhary, A., Gupta, I., Singh, V., & Jana, P. K. (2018). A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing. Future Generation Computer Systems, 83, 14-26. doi:10.1016/j.future.2018.01.005 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search that gives every member a mass from its fitness, accelerates each member by the summed pairwise attractive forces of the others (each force scaling with the product of masses and decaying with separation), and integrates a velocity and position under a coefficient that shrinks over the budget strengths heavier better-scoring members exert more pull, so the population is drawn toward high-quality regions as the coefficient decay sharpens the search
`greedy-completion`	dispatching	makespan, energy, cost	Inspect dispatch rule greedy-completion priority basis state-aware earliest completion next
`grey-wolf-optimizer`	metaheuristic	makespan, energy, cost	Inspect caveats positions move unconditionally each iteration, so the returned order is the best observed rather than a monotone incumbent discrete adaptation Hosseini Shirvani, M. (2022). A Novel Discrete Grey Wolf Optimizer for Scientific Workflow Scheduling in Heterogeneous Cloud Computing Platforms. Scientia Iranica. doi:10.24200/sci.2022.57262.5144 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search drawing every priority vector toward the three best-ranked members with an encircling coefficient that descends linearly over the budget strengths the descending coefficient shifts the search from exploration to exploitation with no per-instance tuning
`guided-local-search`	metaheuristic	makespan, energy, cost	Inspect caveats the (position, task) feature and duration cost are a documented adaptation of the paper's routing-edge feature model evidence class runnable-baseline feature a (position, task) placement whose cost is the task duration mechanism penalty-guided local search that augments the objective with penalties on overused placement features neighborhood best-improvement pairwise swaps on the augmented objective penalty update the maximum-utility placement (cost over one-plus-penalty) is penalized at each local optimum strengths escapes local optima by discouraging the features a stuck optimum keeps reusing, without restarts
`gurobi-exact`	exact	makespan, energy, cost	Inspect commercial true dependency gurobipy dependency extra exact-commercial dependency purpose commercial mixed-integer programming wrapper dependency requirement optional exact method milp license note Gurobi requires a commercial license; a free academic license is available. Never bundled. optimality verification solves-to-optimal-verified
`harris-hawks-optimization`	metaheuristic	makespan, energy, cost	Inspect caveats the besiege moves replace positions unconditionally, so the returned order is the best observed rather than a monotone incumbent discrete adaptation Ali, A., Shah, S. A. A., Al Shloul, T., Assam, M., Ghadi, Y. Y., Lim, S., & Zia, A. (2024). Multiobjective Harris Hawks Optimization-Based Task Scheduling in Cloud-Fog Computing. IEEE Internet of Things Journal. doi:10.1109/JIOT.2024.3391024 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search whose decaying escaping-energy value switches between an exploration phase and four besiege moves around the best member, two of which compare a direct move against a Levy-flight move strengths the energy schedule interleaves exploration and several exploitation intensities without per-instance tuning
`heft`	dispatching	makespan, energy, cost	Inspect mechanism upward-rank list scheduling priority basis nonincreasing upward rank rank basis upward-rank realization rank-ordered dispatch; resource and mode binding by the serial constructor's earliest-finish rule, the non-insertion variant of the published insertion-based earliest-finish-time selection
`ibea`	metaheuristic	makespan, lateness, fairness, energy, cost, carbon	Inspect caveats the pairwise indicator matrix is quadratic in the pool competitor ibea discrete adaptation Frutos, M., Mendez, M., Tohme, F., & Broz, D. (2013). Comparison of multiobjective evolutionary algorithms for operations scheduling under machine availability constraints. The Scientific World Journal, 2013, 418396. doi:10.1155/2013/418396 evidence class runnable-baseline indicator additive epsilon indicator on normalized objectives mechanism binary quality-indicator fitness with the additive epsilon indicator; the least-fit member is removed and the survivors are incrementally credited with its indicator contribution strengths a single tunable indicator drives convergence without reference points, weights, or nondominated sorting
`iterated-greedy-rs`	metaheuristic	makespan, energy, cost	Inspect acceptance fixed-temperature probabilistic acceptance of non-improving sequences caveats acceptance pressure tracks the problem's duration scale evidence class runnable-baseline mechanism destruction-reconstruction search from a best-insertion seed relates to iterated-greedy: the mechanism-diversified competitor realization strengths the canonical published iterated-greedy configuration for permutation scheduling temperature basis temperature_factor times mean task duration over ten, a documented adaptation of the published constant to the scalar-duration task model
`jaya-algorithm`	metaheuristic	makespan, energy, cost	Inspect caveats greedy replacement keeps only improving updates, so progress tracks the population's best and worst spread discrete adaptation Mishra, A. K., & Shrivastava, D. (2020). A discrete Jaya algorithm for permutation flow-shop scheduling problem. International Journal of Industrial Engineering Computations, 11(3), 415-428. doi:10.5267/j.ijiec.2019.12.001 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism parameter-less population search moving each priority vector toward the best and away from the worst member strengths carries no algorithm-specific parameters beyond population size and iteration budget
`l2d-disjunctive-gnn`	learning	makespan, energy, cost	Inspect documented adaptation faithful Graph Isomorphism Network over the disjunctive graph under the adding-arc strategy with the scheduled-indicator and completion-lower-bound raw features; single-instance training, a one-shot initial-state embedding with a greedy order decode instead of the reference per-step re-embedding, and a terminal normalized schedule-score reward adapt the published amortized size-agnostic dispatcher to the platform's per-instance task-order model evidence class runnable-baseline learned paradigm policy-gradient GNN construction (PPO) mechanism GIN disjunctive-graph + PPO dispatching rule optional extra learning policy basis graph-isomorphism network over the disjunctive graph with an average-pooled size-agnostic graph embedding training single-instance proximal policy optimization over seeded rollouts with a terminal normalized-improvement reward
`l2s-improvement`	learning	makespan, energy, cost	Inspect documented adaptation faithful DRL-guided improvement heuristic that starts from a complete feasible schedule, embeds its disjunctive graph with the topological and context-aware modules over the (p, est, lst) node features, and learns N5 critical-block moves by REINFORCE with the incumbent-improvement reward; the N5 neighborhood is realized over the platform's precedence-feasible task order as makespan-binding adjacent same-machine transpositions, and single-instance REINFORCE over a bounded improvement horizon adapts the published amortized training to the platform's per-instance task-order model evidence class runnable-baseline learned paradigm policy-gradient GNN improvement (REINFORCE) mechanism DRL-guided N5 improvement heuristic optional extra learning policy basis graph-neural network with a topological (GIN) module and a context-aware dual-context module over the complete-solution disjunctive graph, scoring N5 critical-block local-search moves training single-instance REINFORCE over the improvement rollout with the incumbent-improvement reward, keeping the best order found
`learned-priority-policy`	learning	makespan, energy, cost	Inspect learned tier disclosure two distinct learned representatives -- this policy-gradient priority rule and the value-based tabular Q-learning agent (rl-dispatching); no post-Decima DRL scheduler has consensus-baseline adoption mechanism policy-gradient learned priority dispatching rule policy basis linear priority over task features training REINFORCE over seeded precedence-feasible rollouts
`logic-based-benders-decomposition`	exact	makespan, energy, cost	Inspect commercial false dependency pulp dependency extra exact dependency purpose logic-based Benders assignment-master ILP adapter dependency requirement optional exact method logic-based-benders license note PuLP is BSD-licensed and ships the CBC open backend; the assignment master is solved through it and the per-facility scheduling subproblem is native pure Python. optimality verification solves-to-optimal-verified
`longest-processing-time`	dispatching	makespan, energy, cost	Inspect dispatch rule longest-processing-time priority basis longest duration first
`lshade`	metaheuristic	makespan, energy, cost	Inspect decode rule random-key encoded solver lshade encoding adapter random-key repair policy topological-normalize transfer identity
`marine-predators`	metaheuristic	makespan, energy, cost	Inspect caveats each iteration costs two schedule evaluations per member (the forage move and the FADs escape) discrete adaptation Chen, D., & Zhang, Y. (2023). Diversity-Aware Marine Predators Algorithm for Task Scheduling in Cloud Computing. Entropy, 25(2), 285. doi:10.3390/e25020285 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search that forages toward the best member by a Brownian phase early, a mixed Brownian/Levy phase in the middle, and a Levy phase late, escaping local optima with a fish-aggregating-device perturbation and keeping only improving moves strengths the phased Brownian-to-Levy schedule plus memory saving gives a monotone incumbent with a strong late-run local search
`matheuristic-restricted-neighbourhood`	metaheuristic	makespan	Inspect activity selection network-dependent: rank by the progressive/regressive-level body rule and select 50% of the activities for intense search envelope classical RCPSP only: finish-start precedence with no lag, renewable resources, single minimise-makespan objective; fails closed on generalized precedence, non-makespan or maximize objectives, and moldable, gang, or partial-execution tasks evidence class runnable-baseline exact neighbourhood the selected subproblem is solved by the native immediate-selection branch-and-bound (Brucker et al. 1998) over the pinned sub-instance future work the paper's higher-performance decomposition genetic-algorithm variant (Section 3.2.3) is not implemented; the faithful greedy-search form is realised instead mechanism greedy-search matheuristic: random-key sampling generates start schedules and an exact branch-and-bound re-optimises a selected activity body as a restricted neighbourhood search never worse the neighbourhood search accepts only improvements over the start schedule, so the result never regresses below the best sampled start schedule; near-optimal, not exact restriction full forward: the non-selected activities are pinned at their current start times as a latest-start bound and the selected subproblem is searched exactly without further truncation
`max-min`	dispatching	makespan, energy, cost	Inspect degenerate when tasks pin all resources through demands mechanism ready-set batch mapping by largest best completion priority basis largest minimum completion next realization the precedence-ready set stands in for the published independent-task batch; candidate and committed bindings follow the serial constructor's earliest-finish rule
`min-min`	dispatching	makespan, energy, cost	Inspect degenerate when tasks pin all resources through demands mechanism ready-set batch mapping by smallest best completion priority basis earliest minimum completion next realization the precedence-ready set stands in for the published independent-task batch; candidate and committed bindings follow the serial constructor's earliest-finish rule
`minimum-slack`	dispatching	makespan, energy, cost	Inspect dispatch rule minimum-slack priority basis least slack first
`moead`	metaheuristic	makespan, lateness, fairness, energy, cost	Inspect caveats weight-grid resolution bounds attainable spread evidence class runnable-baseline mechanism weighted-decomposition subproblems with neighborhood replacement strengths decomposition-based counterweight to dominance-based selection
`monte-carlo-tree-search`	metaheuristic	makespan, energy, cost	Inspect caveats the running best/worst reward normalization is a documented adaptation of UCB1's bounded-reward assumption evidence class runnable-baseline mechanism upper-confidence-bounds-for-trees search that builds a schedule by appending precedence-ready tasks reward the constructed order's score is normalized to [0, 1] against the running best and worst scores for UCB1 rollout partial orders are completed by appending uniformly random precedence-ready tasks selection UCB1 balances the best mean reward against the least-visited child by the exploration constant strengths concentrates simulations on the construction prefixes that have yielded the best schedules
`moth-flame-optimization`	metaheuristic	makespan, energy, cost	Inspect caveats the flame memory guarantees monotone best positions, but each member's spiral is unconditional discrete adaptation Abd Elaziz, M., Abualigah, L., & Attiya, I. (2021). Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments. Future Generation Computer Systems, 124, 142-154. doi:10.1016/j.future.2021.05.026 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search spiralling every member around one of the best positions in a sorted memory whose size shrinks over the budget, keeping the best positions after each spiral strengths the shrinking flame memory retains several elite positions early and converges onto the best late, with no tuning
`ndso-core`	ndso	makespan, energy, cost	Inspect anytime convergence false coupled rate temperature true decayed cell reinjection true encoding natively-discrete method variant ndso-core peer guidance true positional preference model true preference weighted plurality true source selection schedule true three source guidance true update magnitude schedule true
`ndso-fast`	ndso	makespan, energy, cost	Inspect anytime convergence true coupled rate temperature true decayed cell reinjection false encoding natively-discrete method variant ndso-fast peer guidance true positional preference model true preference weighted plurality false source selection schedule true three source guidance false update magnitude schedule true
`ndso-summit`	ndso	makespan, energy, cost	Inspect coordination inter-swarm-council cross swarm confidence weighted voting true encoding natively-discrete method variant ndso-summit overarching elite synthesis true
`neh`	constructive	makespan, energy, cost	Inspect mechanism nonincreasing-duration best-insertion construction priority basis nonincreasing-duration insertion
`nsga2`	metaheuristic	makespan, lateness, fairness, energy, cost	Inspect caveats diversity pressure weakens beyond three objectives evidence class runnable-baseline mechanism nondominated sorting with crowding-distance elitism strengths the default bi/tri-objective baseline in scheduling comparisons
`nsga3`	metaheuristic	makespan, lateness, fairness, energy, cost, carbon	Inspect caveats reference-point count grows with objective dimension competitor nsga3 evidence class runnable-baseline mechanism reference-point niching over nondominated fronts reference point design Das-Dennis structured simplex strengths diversity preservation in many-objective spaces
`ortools-cp-sat`	exact	makespan, energy, cost	Inspect commercial false dependency ortools dependency extra exact dependency purpose exact constraint-programming adapter dependency requirement optional exact method cp-sat license note OR-Tools is Apache-2.0 licensed; no commercial terms. optimality verification solves-to-optimal-verified
`particle-swarm`	metaheuristic	makespan, energy, cost	Inspect mechanism single-task attraction toward the global-best order metaheuristic particle-swarm realization no velocity state or personal-best memory is implemented; the citation marks lineage, not mechanism fidelity scheduling contract permutation-decode-to-schedule
`peft`	dispatching	makespan, energy, cost	Inspect mechanism optimistic-cost-table rank list scheduling priority basis nonincreasing mean optimistic cost rank basis oct-rank realization rank-ordered dispatch; resource and mode binding by the serial constructor's earliest-finish rule, a documented variant of the published O_EFT selection
`pulp-milp`	exact	makespan, energy, cost	Inspect commercial false dependency pulp dependency extra exact dependency purpose mixed-integer linear programming adapter dependency requirement optional exact method milp license note PuLP is BSD-licensed and ships the CBC open backend. optimality verification solves-to-optimal-verified
`residual-scheduling`	learning	makespan, energy, cost	Inspect documented adaptation faithful residual (shrinking, re-embedded) HGIN over unfinished operations; single-instance training, greedy order decode, terminal-objective reward with a running-mean baseline, hidden width reduced 256->128, and uniform per-machine durations adapt the published amortized job-shop scheduler to the platform's per-instance task-order model evidence class runnable-baseline learned paradigm policy-gradient GNN construction (REINFORCE, residual state) mechanism residual heterogeneous-GIN + REINFORCE construction scheduler optional extra learning policy basis residual heterogeneous-GIN over unfinished operations training single-instance REINFORCE over seeded rollouts with a terminal normalized-improvement reward and a running-mean baseline
`rl-dispatching`	learning	makespan, energy, cost	Inspect evidence class runnable-baseline learned paradigm value-based (tabular Q-learning) mechanism tabular Q-learning over dispatching-rule actions policy basis tabular action-value over dispatch rules relates to learned-priority-policy: the policy-gradient learned representative; this is the value-based Q-learning representative state abstraction coarse (scheduling-stage, ready-set-breadth) discretization, a documented adaptation of the published shop-state features training one-step Q-learning with a terminal normalized-improvement reward over seeded epsilon-greedy episodes
`rollout`	constructive	makespan, energy, cost	Inspect base policy shortest-processing-time static-priority completion budget a wall-clock budget degrades the lookahead to the bare base-policy completion of the committed prefix caveats cost grows with the base-policy completions scored, quadratic in the task count evidence class runnable-baseline guarantee the sequentially-consistent base policy makes the rollout order score no worse than the base policy it improves mechanism one-step base-policy lookahead sequential construction relates to shortest-processing-time: the base policy this rollout improves strengths recovers order quality a one-pass dispatch rule leaves on the table by scoring a full completion per ready candidate
`rvea`	metaheuristic	makespan, lateness, fairness, energy, cost, carbon	Inspect caveats reference-vector count grows with objective dimension competitor rvea evidence class runnable-baseline mechanism angle-penalized-distance selection over reference vectors reference vector design unit-normalized Das-Dennis simplex strengths convergence-diversity balance that sharpens over the run
`salp-swarm`	metaheuristic	makespan, energy, cost	Inspect caveats positions move unconditionally each iteration, so the returned order is the best observed rather than a monotone incumbent discrete adaptation Jain, R., & Sharma, N. (2022). A Deadline-Constrained Time-Cost-Effective Salp Swarm Algorithm for Resource Optimization in Cloud Computing. International Journal of Applied Metaheuristic Computing, 13(1). doi:10.4018/IJAMC.292509 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous chain search whose leader is drawn toward the best-so-far food source by a coefficient that decays over the budget, and whose followers each move to the mean of their own and their predecessor's position strengths the follower averaging smooths the chain toward the leader while the decaying coefficient sharpens the leader's search
`sarsa-dispatching`	learning	makespan, energy, cost	Inspect discrete adaptation Aissani, N., Beldjilali, B., & Trentesaux, D. (2009). Dynamic scheduling of maintenance tasks in the petroleum industry: a reinforcement approach. Engineering Applications of Artificial Intelligence, 22(7), 1089-1103. doi:10.1016/j.engappai.2009.01.014; Zhang, Z., Zheng, L., Hou, F., & Li, N. (2011). Semiconductor final test scheduling with Sarsa(lambda, k) algorithm. European Journal of Operational Research, 215(2), 446-458. doi:10.1016/j.ejor.2011.05.052 evidence class runnable-baseline learned paradigm value-based on-policy TD control (SARSA) mechanism tabular on-policy SARSA over dispatching-rule actions policy basis tabular action-value over dispatch rules relates to rl-dispatching: the off-policy Q-learning representative; this is the on-policy SARSA representative training on-policy SARSA that bootstraps toward the next selected action-value, with a terminal normalized-improvement reward over seeded epsilon-greedy episodes
`scatter-search`	metaheuristic	makespan, energy, cost	Inspect caveats path relinking is precedence-repaired over the order space, a documented adaptation of the published combination method combination path relinking between the two reference orders of each subset competitor scatter-search evidence class runnable-baseline mechanism reference-set path-relinking search over precedence-safe orders realizes the elite-solution path relinking left unrealized in critical-path-tabu reference set a best-scoring quality subset plus a max-min positional-distance diversity subset strengths systematic recombination of diverse high-quality task orders
`selection-hyper-heuristic`	metaheuristic	makespan, energy, cost	Inspect acceptance late-acceptance hill-climbing against the cost recorded a fixed number of steps earlier evidence class runnable-baseline low level heuristics adjacent-swap, insertion-shift, relink-to-incumbent, bounded-local-search mechanism two-layer selection hyper-heuristic: adaptive heuristic selection over a fixed low-level heuristic set with a separate move-acceptance criterion relates to distinct from ccgp, a generation hyper-heuristic that evolves priority-rule trees rather than selecting among fixed low-level heuristics scheduling contract permutation-decode-to-schedule selection reinforcement-style adaptive credit; improving moves reward the applied heuristic, non-improving moves penalize it, with roulette-wheel proportional selection
`serial-sgs-justification`	constructive	makespan, energy, cost	Inspect caveats single-pass constructor; no search beyond the justification re-decodes; on unit-capacity (or demand-equals-capacity) problems the cumulative decode reduces to the scalar exclusive timeline citation doi pending verified publisher landing link recorded as the uri; the DOI digits are unverified and await operator confirmation evidence class runnable-baseline mechanism latest-finish-time serial construction with double justification, decoded through the cumulative capacity-profile placement so a resource packs concurrent activities up to its capacity realization double justification realized as finish- and start-sorted re-decodes through the serial constructor, a documented order-space adaptation of the published schedule-space shifts strengths justification improves serial-decoder schedules at the cost of only two extra decodes; the cumulative decode honors the capacity-aware capability on resources carrying demand below capacity
`shifting-bottleneck`	constructive	makespan, energy, cost	Inspect caveats heads and tails are longest-path bounds treating dependency lags as finish-to-start offsets; the serial constructor honors the exact timing evidence class runnable-baseline machine identity a distinct resource-demand set; demand-free tasks share a pool machine mechanism machine decomposition sequencing the bottleneck machine first reoptimization committed machines re-sequenced while later commitments tighten their release dates and tails strengths critical-path decomposition that resolves the most constraining resource before the rest subproblem single-machine release-date relaxation solved by Schrage's rule
`shortest-processing-time`	dispatching	makespan, energy, cost	Inspect dispatch rule shortest-processing-time priority basis shortest duration first
`simulated-annealing`	metaheuristic	makespan, energy, cost	Inspect mechanism adjacent-swap neighborhood under Metropolis acceptance metaheuristic simulated-annealing realization single-chain anneal, temperature cooling linearly to a 0.01 floor; faithful in kind to the cited scheme scheduling contract permutation-decode-to-schedule
`sine-cosine-algorithm`	metaheuristic	makespan, energy, cost	Inspect caveats positions move unconditionally each iteration, so the returned order is the best observed rather than a monotone incumbent discrete adaptation Jouhari, H., Lei, D., Al-qaness, M. A. A., Abd Elaziz, M., Ewees, A. A., & Farouk, O. (2019). Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times. Mathematics, 7(11), 1120. doi:10.3390/math7111120 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search nudging every priority vector toward the best-so-far destination by a sine- or cosine-weighted step whose amplitude descends linearly over the budget strengths the oscillating step spans a wide region early and collapses onto the destination late with no per-instance tuning
`slim-self-labeling`	learning	makespan, energy, cost	Inspect documented adaptation faithful Pointer-Network-style encoder-decoder generative model trained by the self-labeling cross-entropy over the best sampled pseudo-label with neither an MDP nor reinforcement learning; the fifteen graph-node and eleven job-context features collapse to the platform-derivable subset, and single-instance self-labeling over a bounded step budget with a greedy decode adapts the published dataset-scale amortized training to the platform's per-instance task-order model evidence class runnable-baseline learned paradigm self-supervised generative construction mechanism pointer-network generative model + self-labeling optional extra learning policy basis pointer-network-style encoder-decoder generative model over the disjunctive graph with a multi-head-attention memory network training single-instance self-labeling: sample solutions, adopt the minimum-makespan sample as a pseudo-label, and minimize the cross-entropy toward it, using no reward or Markov Decision Process
`sms-emoa`	metaheuristic	makespan, lateness, fairness, energy, cost, carbon	Inspect caveats hypervolume cost grows with the objective count competitor sms-emoa evidence class runnable-baseline indicator exact dominated hypervolume via objective slicing mechanism dominated-hypervolume (S-metric) survival selection strengths reference-free spread and convergence balance
`spea2`	metaheuristic	makespan, lateness, fairness, energy, cost, carbon	Inspect caveats the pairwise distance matrix is quadratic in the pool competitor spea2 density k-th nearest neighbour with k = floor(sqrt N) discrete adaptation Wei, Feng, Tan, and Hagiwara. Flexible job shop scheduling multi-objective optimization based on an improved strength Pareto evolutionary algorithm. Advanced Materials Research, 186, 546-551, 2011. doi:10.4028/www.scientific.net/AMR.186.546 evidence class runnable-baseline mechanism strength-and-raw-fitness ranking with a k-th nearest-neighbour density estimator and distance-based archive truncation strengths the density estimator preserves a well-spread archive without reference points or scalarizing weights
`squeaky-wheel`	metaheuristic	makespan, energy, cost	Inspect analyzer blame equals the task completion time read from the constructed schedule assignments caveats the completion-time blame is a documented adaptation of the paper's domain-specific trouble measure evidence class runnable-baseline initialization duration-normalized prior with a seeded uniform perturbation of the starting priority ordering mechanism construct/analyze/prioritize search over a per-task priority vector that induces the construction sequence prioritizer blamed tasks' priorities rise by bump_strength times their makespan-normalized blame, pulling late tasks earlier strengths reprioritizes around the tasks that finish latest rather than searching the order space blindly
`sufferage`	dispatching	makespan, energy, cost	Inspect degenerate when tasks pin all resources through demands mechanism ready-set batch mapping by completion-loss gap priority basis largest second-best-minus-best completion gap next realization the precedence-ready set stands in for the published independent-task batch; candidate and committed bindings follow the serial constructor's earliest-finish rule
`surrogate-assisted-gp-hh`	learning	makespan, energy, cost	Inspect documented adaptation single static instance -> derived related tasks (makespan, lateness-weighted, flow-weighted scorings); 1-NN phenotypic-characterisation surrogate over ready-set reference situations; bounded population and generations for single-instance latency evidence class runnable-baseline extends ccgp learned paradigm genetic-programming hyper-heuristic (surrogate multitask) mechanism surrogate-assisted multitask GP hyper-heuristic policy basis evolved priority-rule trees decoded by precedence-safe list scheduling relates to ccgp: the cooperative-coevolution GP hyper-heuristic this surrogate multitask method extends training each generation a per-task 1-NN phenotypic-characterisation surrogate screens the merged offspring pool; only the reallocated survivors are really evaluated
`tabu-mrcpsp-mode-search`	metaheuristic	makespan, energy, cost	Inspect adaptation operates on the renewable-only, finish-start projection: the paper's generalized precedence relations (max lags, SF/FF), non-renewable and doubly constrained resources, and its inefficient-mode preprocessing are outside the core model and are projected away, not fabricated aspiration asymmetric: global and regional aspiration by objective override tabu status; influence always tilts move selection evidence class runnable-baseline inner scheduler a fixed mode assignment is projected onto the problem (each activity pinned to one mode) and decoded by the shared serial constructor, standing in for the paper's truncated RCPSP-GPR branch-and-bound long term memory frequency-based diversification and intensification phase switching after an initial data-collection phase mechanism tabu search over the mode-assignment neighborhood: each move reassigns one activity to another execution mode neighborhood single-activity re-mode -- all assignments differing from the incumbent in exactly one activity's mode tabu list dynamic random tenure in [sqrt(n), 3*sqrt(n)] on the reversing (activity, mode) attribute
`teaching-learning-optimization`	metaheuristic	makespan, energy, cost	Inspect caveats greedy replacement keeps only improving updates, so progress tracks the population's best member and mean discrete adaptation Xie, Z., Zhang, C., Shao, X., Lin, W., & Zhu, H. (2014). An effective hybrid teaching-learning-based optimization algorithm for permutation flow shop scheduling problem. Advances in Engineering Software, 77, 35-47. doi:10.1016/j.advengsoft.2014.07.006 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism parameter-less two-phase population search: a teacher phase draws every learner toward the best member and away from the population mean, and a learner phase draws it toward or away from a random peer by which is better strengths carries no algorithm-specific parameters beyond population size and iteration budget
`whale-optimization`	metaheuristic	makespan, energy, cost	Inspect caveats positions move unconditionally each iteration, so the returned order is the best observed rather than a monotone incumbent discrete adaptation Zhang, S., & Gu, X. (2023). A discrete whale optimization algorithm for the no-wait flow shop scheduling problem. Measurement and Control. doi:10.1177/00202940231180622 encoding continuous priority vector decoded to a permutation by the Largest Order Value rule, then precedence-repaired evidence class runnable-baseline mechanism continuous population search choosing per iteration a spiral move around the best member, an encircling move toward it, or a search move toward a random member, under a control value that descends linearly over the budget strengths the spiral and search moves preserve exploration while the descending control value sharpens exploitation, with no per-instance tuning

All objective support is declared registry metadata, not a performance claim. The solver recommender ranks these solvers by declared fit.

Applicabilité des solveurs

Quels solveurs s'appliquent à quelle famille de référence, lu depuis la matrice d'applicabilité. Chaque cellule est une applicabilité déclarée, jamais une affirmation de performance : les cellules vérifiées citent une campagne publique, une citation ou un test nommé, et les cellules approximatives sont dérivées des capacités déclarées du solveur et des traits de la famille de référence. Le tableau ci-dessous est généré à partir du paquet public d'applicabilité, de sorte que ses totaux de statut sont dénombrables à partir des lignes elles-mêmes.

Generated from the applicability matrix: 69 benchmark families by 87 public solvers. 10 verified · 5835 approximate · 158 not applicable.

Cell status is declared applicability, never a performance claim. Verified cells cite a named public campaign artifact, citation source, or test; approximate cells are derived from declared solver capabilities and benchmark-family traits and are labeled as such.

SearchBenchmark familySolverStatus

Showing 6003 of 6003 cells.

Benchmark-family by solver applicability — 6003 matching rows, showing page 1 of 121; build-inlined from the public applicability bundle.
			Basis
`accelerator-coscheduling`distributed-computing	`adpso`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; searches a continuous space decoded via the random-key-adapter with topological repair.
`accelerator-coscheduling`distributed-computing	`age-moea-ii`	Approximate	family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`ant-colony`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`apparent-tardiness-cost`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`arithmetic-optimization`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`artificial-bee-colony`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`artificial-fish-swarm`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`beam-search`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`branch-and-bound`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`ccgp`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`clpso`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; searches a continuous space decoded via the random-key-adapter with topological repair.
`accelerator-coscheduling`distributed-computing	`cma-es`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`cpop`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`critical-path-tabu`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`cuckoo-search`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`d-clpso`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; searches a continuous space decoded via the spv-adapter with topological repair.
`accelerator-coscheduling`distributed-computing	`d-depso`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`d-lshade`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; searches a continuous space decoded via the rounding-adapter with topological repair.
`accelerator-coscheduling`distributed-computing	`dan-dual-attention`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`decima-dag-rl`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`decision-diagram-sequencing`	Not applicable	native exact search caps at 12 tasks; family instances carry 96; optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`differential-evolution`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`earliest-deadline`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`earliest-finish-time`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`earliest-start`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`epso`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; searches a continuous space decoded via the random-key-adapter with topological repair.
`accelerator-coscheduling`distributed-computing	`exhaustive-enumeration`	Not applicable	native exact search caps at 8 tasks; family instances carry 96; optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`firefly-algorithm`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`fjsp-hgnn-drl`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`genetic-algorithm`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`grasshopper-optimization`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`gravitational-search`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`greedy-completion`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`grey-wolf-optimizer`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`guided-local-search`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`gurobi-exact`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`harris-hawks-optimization`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`heft`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`ibea`	Approximate	family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`iterated-greedy-rs`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`jaya-algorithm`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`l2d-disjunctive-gnn`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`l2s-improvement`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`learned-priority-policy`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`logic-based-benders-decomposition`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`longest-processing-time`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.
`accelerator-coscheduling`distributed-computing	`lshade`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; searches a continuous space decoded via the random-key-adapter with topological repair.
`accelerator-coscheduling`distributed-computing	`marine-predators`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling.
`accelerator-coscheduling`distributed-computing	`matheuristic-restricted-neighbourhood`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity; produces a static schedule for a family with dynamic arrivals/rescheduling; no repair or dispatching mechanism for the family's declared uncertainty.
`accelerator-coscheduling`distributed-computing	`max-min`	Approximate	optimizes one objective axis of a multi-objective family; family declares constraint features with no capability-tag counterpart: affinity.

Cell status is declared applicability, never a performance claim. Verified cells cite a named public campaign, citation, or test; approximate cells are derived from declared solver capabilities and benchmark-family traits. The solver recommender scores solvers against this matrix by scheduling family.

Lignes-de-base d'ordonnancement

Les lignes-de-base déterministes empaquetées sont des solveurs constructifs d'ordonnancement-par-listes. Chacun contrôle l'ordre des tâches ; le constructeur série honore les demandes de ressources déclarées de chaque tâche et affecte chaque tâche à sa ressource demandée disponible-au-plus-tôt. Chaque famille nomme sa référence séminale canonique.

Solveur	Base de priorité	Référence canonique
`earliest-start`	ordre topologique d'entrée	non applicable (ligne-de-base identité)
`shortest-processing-time`	tâche de durée la plus courte d'abord	Smith (1956)
`longest-processing-time`	tâche de durée la plus longue d'abord	Graham (1969)
`earliest-deadline`	deadline le plus tôt d'abord (conscient-du-deadline)	Jackson (1955)
`earliest-finish-time`	fin atteignable la plus tôt d'abord	Topcuoglu et al. (2002)
`minimum-slack`	plus petite marge d'ordonnancement d'abord	Conway, Maxwell & Miller (1967)
`greedy-completion`	achèvement conscient-de-l'état le plus tôt d'abord	Graham (1966)
`apparent-tardiness-cost`	indice de coût-de-retard-apparent le plus élevé d'abord (conscient-du-deadline)	Vepsalainen & Morton (1987)

Familles d'ordonnancement retirées

Les familles de mapping-de-ressources sont enregistrées explicitement plutôt que repliées silencieusement dans un type générique. Elles contrôlent l'affectation des ressources, non l'ordre des tâches, et le constructeur série respectueux-des-demandes n'expose aucune décision de mapping libre sous le modèle de problème cœur actuel :

Mapping de ressources (olb, met, mct, round-robin, load-balanced) : le constructeur affecte chaque tâche à sa ressource déclarée, de sorte que ces heuristiques n'ont aucun degré de liberté réalisable ; le mapping conscient-du-temps-d'exécution est la propriété du travail sur les objectifs et contraintes, non de cette fondation.
Étiquettes de type absentes (type-aware, domain-aware) : requièrent des étiquettes de type-de-tâche et type-de-ressource que le modèle de problème cœur actuel ne porte pas.

retired_dispatching_families() renvoie le grand-livre complet avec justification et citation par-famille.

Ordonnanceurs par listes basés sur le rang

Trois ordonnanceurs par listes conscients-de-l'hétérogénéité partagent une forme à deux phases : une phase de priorisation statique classe chaque tâche sur le DAG de précédence à partir des temps d'exécution moyens et des coûts de communication, et une phase de sélection-de-processeur lie chaque tâche dans l'ordre de priorité via la règle de fin-la-plus-tôt du constructeur série. Les temps d'exécution par-ressource proviennent des modes d'exécution déclarés d'une tâche moldable, et une tâche rigide dégénère en la spécialisation homogène documentée.

Solveur	Base de priorité	Référence canonique
`heft`	rang ascendant	Topcuoglu, Hariri & Wu (2002)
`cpop`	rang ascendant-plus-descendant combiné, tâches du chemin-critique d'abord	Topcuoglu, Hariri & Wu (2002)
`peft`	rang de table-de-coût-optimiste	Arabnejad & Barbosa (2014)

Règles de mapping de l'ensemble-prêt

Trois règles de mapping par lots notent chaque tâche prête-en-précédence par son achèvement le plus tôt sur ses liaisons candidates, puis mappent une tâche par étape. Les règles publiées mappent un lot de tâches indépendantes ; ici le lot est l'ensemble des tâches prêtes-en-précédence, de sorte que les règles s'étendent aux charges de travail dépendantes et se réduisent au comportement publié sur les instances à tâches-indépendantes. Ces familles ont quitté le grand-livre des retirées pour un enregistrement de premier-rang une fois que les TaskSpec.modes moldables ont rendu représentable leur matrice d'achèvement-attendu par-machine.

Solveur	Base de priorité	Référence canonique
`min-min`	plus petit meilleur achèvement d'abord	Ibarra & Kim (1977); Braun et al. (2001)
`max-min`	plus grand meilleur achèvement d'abord	Ibarra & Kim (1977); Braun et al. (2001)
`sufferage`	plus grand écart d'achèvement deuxième-meilleur-moins-meilleur d'abord	Maheswaran et al. (1999)

Lignes-de-base iterated-greedy, tabu, et RCPSP

Trois méthodes publiées à solution-unique cherchent l'espace des ordres-de-tâches sûrs-en-précédence, chacune projetant son mécanisme canonique sur la couture ordre-exécution partagée. serial-sgs-justification est le premier solveur d'ordonnancement-de-projet sous-contrainte-de-ressources (RCPSP) de la plateforme.

Solveur	Mécanisme	Référence canonique
`iterated-greedy-rs`	amorce NEH avec une boucle de destruction-reconstruction et acceptation à température-fixe	Ruiz & Stutzle (2007)
`critical-path-tabu`	recherche tabou avancée sur les mouvements de blocs du chemin-critique	Nowicki & Smutnicki (2005)
`serial-sgs-justification`	décodage par schéma-de-génération-d'ordonnancement série avec double justification droite-puis-gauche	Valls, Ballestin & Quintanilla (2005)

Lignes-de-base métaheuristiques

Les lignes-de-base métaheuristiques ensemencées représentatives partagent les mêmes opérateurs de faisabilité, réparation, notation, et recherche-locale, chacune avec sa référence canonique : genetic algorithm (Holland 1975), simulated annealing (Kirkpatrick et al. 1983), ant colony (Dorigo, Maniezzo & Colorni 1996), particle swarm (Kennedy & Eberhart 1995), et differential evolution (Storn & Price 1997).

Solveurs adaptateurs encodés

La comprehensive learning particle swarm optimization et la success-history adaptive differential evolution cherchent un vecteur continu à valeurs-réelles avec une composante par tâche. Chaque cœur est générique sur un objectif continu, de sorte que son comportement de convergence est validé directement sur un benchmark continu, et est lié au problème d'ordonnancement via un adaptateur d'encodage qui décode un vecteur en un ordre de tâches faisable-en-précédence.

Solveur	Inspiration	Adaptation	Encodage
`clpso`	Comprehensive learning particle swarm (Liang et al. 2006)	Essaim continu décodé en un ordre de précédence	random-key
`d-clpso`	Comprehensive learning particle swarm (Liang et al. 2006)	Adaptateur discret sur le décodage smallest-position-value	spv
`lshade`	Success-history adaptive DE avec réduction linéaire de population (Tanabe & Fukunaga 2014)	Differential evolution continue décodée en un ordre de précédence	random-key
`d-lshade`	Success-history adaptive DE avec réduction linéaire de population (Tanabe & Fukunaga 2014)	Adaptateur discret sur l'arrondi de rang-entier	rounding

CLPSO apprend chaque dimension à partir d'un exemplaire de comprehensive-learning plutôt que d'un unique meilleur-global, de sorte que la meilleure aptitude de l'essaim s'améliore monotonement sur un bassin unimodal. L-SHADE adapte les mémoires de croisement et de facteur-d'échelle depuis son historique de succès et réduit la population linéairement jusqu'à un minimum de quatre individus. Les deux rapportent une trajectoire de convergence, et un test de convergence-correcte échoue lorsque la trajectoire observée contredit le comportement de référence publié, séparé des vérifications de reproductibilité-de-graine.

Ces solveurs sont des optimiseurs continus adaptés à un domaine discret via un pont d'encodage ; ce ne sont pas des réimplémentations exactes d'un quelconque code antérieur, et aucune affirmation de performance n'est faite avant que les campagnes comparatives ne s'exécutent.

Adaptateurs d'encodage

Chaque encodage continu-à-discret nommé est un adaptateur distinct avec une politique de réparation explicite, de sorte qu'aucun encodage n'est replié silencieusement dans un décodeur générique. Chaque adaptateur répare son ordre décodé en un ordre faisable-en-précédence et enregistre si la réparation s'est déclenchée.

Adaptateur	Transfert	Règle de décodage	Statut de citation
`random-key`	identité	trier par clé bornée	canonique (Bean 1994)
`spv`	identité	smallest position value	canonique (Tasgetiren et al. 2007)
`rounding`	identité	créneaux de rang-entier	non-applicable
`sigmoid`	sigmoïde en-S	tirage probabiliste pondéré	canonique (Kennedy & Eberhart 1997)
`v-shaped`	magnitude en-V	tirage probabiliste pondéré	canonique (Mirjalili & Lewis 2013)
`tanh`	tangente hyperbolique décalée	tirage probabiliste pondéré	canonique (Mirjalili & Lewis 2013)

Les décodages déterministes (random-key, spv, rounding) ignorent la source aléatoire ; les décodages de fonction-de-transfert consomment une source ensemencée et se rejouent sous une graine fixe. L'adaptateur rounding enregistre citation-non-applicable car l'arrondi de rang à-l'entier-le-plus-proche est une discrétisation générique sans origine séminale canonique unique.

Ensemble diversifié de concurrents

L'ensemble diversifié de concurrents élargit la comparaison au-delà des lignes-de-base représentatives avec une petite poignée de pairs récents et forts, chacun un solveur nommé avec sa référence texte-intégral projetée sur l'espace de décision ordre-uniquement, plutôt qu'une liste volumineuse de lignes-de-base classiques. Un pair ne reste sur le terrain public que lorsqu'il franchit une barre de force-de-lieu : publié dans la fenêtre de validité post-2020 dans un lieu indexé et évalué-par-les-pairs, avec le niveau du lieu enregistré pour qu'un lecteur le pondère sur la preuve plutôt que sur le prestige.

Solveur	Mécanisme	Lieu	Citation
`epso`	Essaim à initialisation-biaisée-par-charge avec rassemblement de chemins	Electronics (MDPI), 2023 — indexé	Anbarkhan & Rakrouki (2023)
`adpso`	Recherche par essaim à inertie descendante adaptative-au-succès	Sensors (MDPI), 2022 — indexé	Nabi et al. (2022)
`ccgp`	Coévolution coopérative d'arbres de règles-de-priorité	Computers & Operations Research (Elsevier), 2024 — OR de premier-rang	Zaki et al. (2024)

Chaque pair porte des forces, des mises-en-garde, et une classe de preuve de ligne-de-base-exécutable dans ses métadonnées afin que le recommandeur puisse expliquer pourquoi un solveur convient à un contexte. Un ensemble plus large de lignes-de-base classiques — genetic algorithms à clé-entière, biased-random-key, et estimation-of-distribution, et recherches large-neighborhood, iterated greedy, tabu, variable-neighborhood, et memetic — reste enregistré pour comparaison interne mais est tenu hors du terrain public, puisque les heuristiques représentatives portent déjà leur signal de mécanisme. Aucune affirmation de performance n'est faite avant que les campagnes comparatives ne s'exécutent.

Ancre de validité

L'ensemble de concurrents et de travaux-connexes est positionné contre les travaux de la décennie-actuelle via une ancre de validité post-2020 : Karimi-Mamaghan, Mohammadi, Pasdeloup, et Meyer (2023, apprendre à sélectionner des opérateurs via Q-learning intégré dans iterated greedy pour le permutation flowshop, European Journal of Operational Research 304(3):1296-1330, doi:10.1016/j.ejor.2022.03.054) et l'algorithme évolutionnaire multi-objectif collaboratif guidé-par-indicateur de 2024 IEEE Transactions on Evolutionary Computation pour l'ordonnancement de groupes de distributed flowshop (doi:10.1109/TEVC.2023.3339558).

Concurrents multi-objectifs

Deux concurrents Pareto font évoluer une population d'ordres de tâches sûrs-en-précédence sur un vecteur multi-objectif, distincts de la surface de nombreux-objectifs nsga3. nsga2 est le concurrent basé-sur-la-dominance — tri rapide des non-dominés avec départage par distance-de-foule — et moead est le contrepoids basé-sur-la-décomposition, scindant le problème en sous-problèmes scalaires de Tchebycheff le long d'une grille de poids simplexe structurée et remplaçant les titulaires à travers le voisinage de poids-le-plus-proche de chaque sous-problème.

Solveur	Mécanisme	Référence canonique
`nsga2`	Pareto basé-sur-la-dominance : tri rapide des non-dominés, distance de foule	Deb et al. (2002)
`moead`	Pareto basé-sur-la-décomposition : scalarisation de Tchebycheff sur une grille de poids simplexe	Zhang & Li (2007)

Concurrent de nombreux-objectifs

nsga3 est le concurrent de nombreux-objectifs nommé (Deb & Jain 2014, doi:10.1109/TEVC.2013.2281535), distinct de la surface de concurrent bi-objectif. Il maintient une population sur des ordres de tâches sûrs-en-précédence, évalue chaque ordre sur un vecteur multi-objectif (makespan, retard, et équité de charge), et survit à chaque génération par une sélection par nichage de points-de-référence sur des points-de-référence structurés de Das & Dennis sur le simplexe unitaire. La conception des points-de-référence, l'association des solutions à leur direction de référence la plus proche, et la sélection par comptage-de-niche sont la signature algorithmique ; le solveur déclare la capacité many-objective aux côtés de multi-objective afin qu'une campagne puisse le sélectionner explicitement.

Appariement de capacités

SolverRegistry.select apparie les solveurs par capacité, contrainte, et objectif. Les exigences de capacité et de contrainte sont toutes deux exprimées comme des étiquettes de capacité et appariées comme une conjonction : une campagne de nombreux-objectifs qui a aussi besoin de conscience-de-deadline passe required_capabilities=(SolverCapability.MANY_OBJECTIVE,) et required_constraints=(SolverCapability.DEADLINE_AWARE,), et le registre renvoie uniquement les solveurs qui déclarent les deux. Un filtre objective restreint en outre le résultat aux solveurs qui déclarent le support de cet objectif nommé.

Famille NDSO

La famille NDSO est la famille de solveurs à encodage-natif du registre : elle cherche directement sur des ordonnancements faisables. Chaque ordonnancement qu'elle produit est faisable par construction (un constructeur valide-par-conception construit un ordre respectueux-de-précédence à chaque étape), de sorte que la famille porte l'encodage native et n'exécute jamais une étape d'encodage/décodage ni une passe de réparation. La famille compose un petit ensemble de mécanismes nommés :

Confidence Matrix — un magasin clairsemé de confiance apprise par cellule (position, tâche), mis à jour à mesure que de meilleurs ordonnancements renforcent leurs cellules.
Confidence-Weighted Voting — synthétise l'ordonnancement élite en votant à travers la population pondérée par la Confidence Matrix ; la variante rapide utilise un vote de majorité non-pondéré à la place.
Ordonnancements Quantity-and-Quality — l'ordonnancement Quantity fixe combien un candidat change ; l'ordonnancement Quality fixe de quelle source de guidage il apprend.
Guidage à trois-sources — un candidat apprend de l'élite synthétisé (exploitation), d'un pair (diversité), ou d'une source de connaissance-évanouie (exploration radicale).
Coefficient adaptatif unifié — un ordonnancement non-linéaire fait glisser la famille de l'exploration vers l'exploitation et pilote à la fois la sensibilité et le foyer d'apprentissage ; la variante rapide le fixe à une valeur fixe.

Variante	Composition
`ndso-core`	Confidence-Weighted Voting, coefficient adaptatif, guidage à trois-sources
`ndso-fast`	synthèse par vote-de-majorité, coefficient fixe, source de guidage unique
`ndso-summit`	Conseil Inter-Essaim coordonnant plusieurs essaims avec synthèse surplombante

Conseil Inter-Essaim

La variante ndso-summit est la composition de qualité : elle exécute plusieurs essaims en parallèle et les coordonne via un seul conseil. Chaque essaim compose les mécanismes cœur sur sa propre population ; le conseil Inter-Essaim maintient ces essaims travaillant comme une seule recherche plutôt que plusieurs exécutions isolées et synthétise leurs résultats en un unique élite surplombant — l'ordonnancement que tout le sommet soutient. Le conseil est ce qui distingue la variante au premier coup d'œil : ndso-core et ndso-fast cherchent chacun avec une population, tandis que ndso-summit est la composition construite pour la recherche multi-essaim coordonnée.

Le comportement de coordination du conseil et les diagnostics par-essaim sont configurables, et chaque ordonnancement qu'il produit reste faisable par construction.

Carte d'ablation

La carte d'ablation énumère une configuration isolante par mécanisme nommé afin que l'analyse en aval puisse attribuer la contribution de chaque mécanisme. Les entrées intra-essaim désactivent chacune un interrupteur cœur — la Confidence Matrix, la Confidence-Weighted Voting, l'ordonnancement Quantity, l'ordonnancement Quality, la structure de guidage multi-source, la source pair, la source de connaissance-évanouie, et le coefficient adaptatif. Les entrées de coordination désactivent chacune un paramètre du conseil — la coordination Intra- versus Inter-Essaim et la synthèse surplombante. Chaque entrée déclare le plancher de comptage-d'exécutions auquel la couche d'analyse échantillonne sa comparaison et nomme les tests statistiques que cette couche applique (un test de significativité non-paramétrique apparié, un post-hoc de rang-moyen de Friedman avec une correction de comparaison-multiple de Holm, et une taille-d'effet Cliff's-delta). La comparaison échantillonnée est matérialisée par le moteur d'expériences en aval ; l'exécuteur en-processus prouve que chaque isolation est faisable.

Un filtre d'export par-étapes gouverne quels mécanismes une portée de rapport peut exposer : la portée de base expose uniquement les mécanismes fondationnels, et un gate fail-closed lève plutôt que de fuiter un mécanisme plus-rapide ou de plus-haute-qualité sous une portée de base, de sorte que les portées fondationnelle et rapide ne peuvent exposer les mécanismes réservés-au-conseil. Les diagnostics de convergence — diversité de population, entropie de la Confidence-Matrix, ratio d'exploration, charge de ressources, chronométrage, et la trace d'amélioration — sont optionnels et n'ajoutent aucune surcharge quand ils sont désactivés, et le conseil exporte un manifeste JSON que la couche d'analyse ingère sans importer aucun type de solveur.

Adaptateurs exacts

Les adaptateurs exacts prennent l'une de deux formes. Les adaptateurs de backend-optionnel déclarent une dépendance optionnelle et vérifient sa racine d'importation à l'exécution ; si le backend est absent ils lèvent MissingOptionalDependencyError avec le nom de l'extra, le but, le drapeau commercial, et la note de licence plutôt que d'importer une dépendance lourde durant l'importation du paquet. Les adaptateurs natifs-bornés ne portent aucune dépendance tierce et résolvent exactement les petites instances par leur propre recherche bornée — en énumérant des ordres faisables-en-précédence dans un cas, branch-and-bound par diagramme-de-décision dans l'autre —, levant UnsupportedCapabilityError lorsque l'instance dépasse le compte de tâches pris-en-charge.

Solveur	Méthode	Backend	Extra	Commercial
`ortools-cp-sat`	CP-SAT	`ortools`	`exact`	non
`pulp-milp`	MILP / MIP	`pulp`	`exact`	non
`branch-and-bound`	branch-and-bound / cut	`ortools`	`exact`	non
`logic-based-benders-decomposition`	décomposition de Benders basée-sur-la-logique	`pulp`	`exact`	non
`exhaustive-enumeration`	énumération exhaustive	natif (sans backend)	—	non
`decision-diagram-sequencing`	branch-and-bound par diagramme-de-décision	natif (sans backend)	—	non
`gurobi-exact`	MILP / MIP	`gurobipy`	`exact-commercial`	oui

La poignée exacte publique est l'ensemble ouvert représentatif — CP-SAT, MILP, branch-and-bound, et décomposition de Benders basée-sur-la-logique — plus les deux solveurs natifs bornés et le wrapper Gurobi authentiquement invoquant. Le wrapper commercial Gurobi reste derrière l'extra exact-commercial, n'est jamais empaqueté, et porte une note de licence explicite ; une licence académique est disponible. Un pool plus large de backends optionnels reste enregistré pour usage interne.

Couche de sélection et d'apprentissage

La couche de sélection classe les solveurs pour un problème sans jamais affirmer un solveur globalement meilleur. Elle consomme un enregistrement SelectionFeatures — difficulty, heterogeneity, objective_conflict, uncertainty, dynamism, et solver_sensitivity — et les métadonnées publiques de solveur, et renvoie des lignes SolverRecommendation classées. Chaque recommandation porte un RecommendationSource (metadata ou learned-model), un ConfidenceLabel, et des notes de limitation explicites, de sorte qu'une recommandation de métadonnées n'est jamais confondue avec une apprise.

FEATURE_ORIGIN trace chaque caractéristique jusqu'à la métrique de caractérisation de benchmark qu'elle lit ; le consommateur mappe les métriques de caractérisation de dispatchatlas.bench dans le contrat de caractéristiques, de sorte que dispatchatlas.solve importe toujours dispatchatlas.core uniquement. Deux sélecteurs sont livrés : RuleBasedSelector classe depuis les capacités déclarées et la caractérisation seule (une recommandation de métadonnées), et SupervisedSelector classe depuis un corpus étiqueté par un modèle déterministe de plus-proche-voisin pondéré-par-distance (une recommandation de modèle-appris).

Le sélecteur supervisé rapporte la généralisation hors-échantillon, jamais l'ajustement d'entraînement. Le protocole de validation-croisée partitionne un corpus étiqueté de sorte qu'aucune instance, aucune famille de benchmark, et aucun enregistrement de caractérisation n'apparaisse dans les deux partitions d'entraînement et de test (partition_by_families, leave_one_family_out, leakage_report), s'entraîne sur les familles restantes, et note la famille retenue (held_out_generalization, cross_validate). Sa confiance s'élève au-dessus de métadonnées-seules uniquement lorsqu'une évaluation retenue exempte-de-fuite la soutient.

learning_interface_catalog() enregistre sept interfaces d'apprentissage et hybrides nommées — sélection d'algorithme supervisée, recherche assistée-par-substitut, hooks d'apprentissage-par-renforcement, hyper-heuristiques, réparation guidée-par-politique, initialisation apprise, et une ligne-de-base benchmark-uniquement. Chacune déclare une LearningEvidencePolicy (données d'entraînement, contrôles de fuite, reproductibilité, classe de preuve, éligibilité de niveau-de-preuve). Deux sont implémentées ici ; les cinq autres sont des interfaces différées enregistrées dont la réalisation est conditionnée aux campagnes comparatives qui produisent des données d'entraînement. Les backends d'estimateur lourds restent derrière l'extra optionnel learning et sont sondés par racine d'importation, jamais importés durant l'importation du paquet ; l'absence lève MissingOptionalDependencyError tandis que le repli déterministe reste disponible.

Garde-fous de performance

La notation par lots est explicite via BatchScoringProfile. Le kernel actuel utilise un repli de bibliothèque-standard et enregistre ce repli dans les diagnostics. Cela maintient l'API prête pour des kernels vectorisés ou accélérés tout en préservant un chemin testé et portable.