Sistema de solvers

dispatchatlas.solve detém os metadados de solvers, a seleção, a construção de escalonamentos, o reparo, as salvaguardas de desempenho, as famílias de linha-base, os adaptadores opcionais, e a família NDSO.

O pacote importa apenas dispatchatlas.core. Os testes de integração de fumaça com benchmarks vivem em tests/solve/ para que o pacote de runtime não dependa de geradores de benchmarks concretos.

Exemplo executável: examples/compare_solvers.py escalona uma coorte de solvers em paralelo e a ancora com um ótimo exato comprovado.

Registro

SolverRegistry armazena fábricas de solvers sem-estado com metadados ricos:

rótulos de capacidade como capacity-aware, precedence-aware, repair, local-search, e ndso
objetivos suportados como makespan, energy, e cost
restrições declaradas expressas por meio de rótulos de capacidade
critérios de parada padrão
comportamento de replay determinístico ou estocástico-semeado
codificação de solução (permutation, mapping, assignment, ou native)
declarações de dependência opcionais e divulgação de backend-comercial
visibilidade de nível-de-evidência para exportações escalonadas
uma citação canônica, ou uma justificativa explícita de citação-não-aplicável

Cada família de solver nomeada carrega uma citação que falha fechando: uma família com uma origem seminal canônica que omite sua referência não pode ser construída, e uma família sem uma única origem canônica registra a razão em vez de fabricar uma.

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

Catálogo do registro

Cada solver do registro, em uma tabela ordenável e pesquisável. A tabela e seus totais são gerados a partir de default_solver_registry(), de modo que as contagens abaixo são contáveis a partir das próprias linhas. Um gráfico de cobertura de capacidades acima da tabela resume quantos solvers do registro declaram cada capacidade declarada.

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.

Aplicabilidade do solver

Quais solvers se aplicam a qual família de benchmark, lido da matriz de aplicabilidade. Cada célula é aplicabilidade declarada, nunca uma alegação de desempenho: células verificadas citam uma campanha pública, citação ou teste nomeado, e células aproximadas são derivadas das capacidades declaradas do solver e das características da família de benchmark. A tabela abaixo é gerada a partir do pacote público de aplicabilidade, portanto seus totais de status são contáveis a partir das próprias linhas.

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.

Linhas-base de despacho

As linhas-base determinísticas empacotadas são solvers construtivos de escalonamento-por-lista. Cada um controla a ordem de tarefas; o construtor serial honra as demandas de recurso declaradas de cada tarefa e atribui cada tarefa ao seu recurso demandado de disponibilidade-mais-cedo. Cada família nomeia sua referência seminal canônica.

Solver	Base de prioridade	Referência canônica
`earliest-start`	ordem topológica de entrada	não aplicável (linha-base de identidade)
`shortest-processing-time`	tarefa de menor duração primeiro	Smith (1956)
`longest-processing-time`	tarefa de maior duração primeiro	Graham (1969)
`earliest-deadline`	deadline mais cedo primeiro (consciente-de-deadline)	Jackson (1955)
`earliest-finish-time`	fim alcançável mais cedo primeiro	Topcuoglu et al. (2002)
`minimum-slack`	menor folga de escalonamento primeiro	Conway, Maxwell & Miller (1967)
`greedy-completion`	conclusão consciente-do-estado mais cedo primeiro	Graham (1966)
`apparent-tardiness-cost`	maior índice de custo-de-atraso-aparente primeiro (consciente-de-deadline)	Vepsalainen & Morton (1987)

Famílias de despacho aposentadas

As famílias de mapeamento-de-recursos são registradas explicitamente em vez de dobradas silenciosamente em um tipo genérico. Elas controlam a atribuição de recursos, não a ordem de tarefas, e o construtor serial respeitoso-da-demanda não expõe nenhuma decisão de mapeamento livre sob o modelo de problema núcleo atual:

Mapeamento de recursos (olb, met, mct, round-robin, load-balanced): o construtor atribui cada tarefa ao seu recurso declarado, de modo que essas heurísticas não têm grau de liberdade realizável; o mapeamento consciente-do-tempo-de-execução é propriedade do trabalho de objetivos e restrições, não desta base.
Rótulos de tipo ausentes (type-aware, domain-aware): requerem rótulos de tipo-de-tarefa e tipo-de-recurso que o modelo de problema núcleo atual não carrega.

retired_dispatching_families() retorna o livro-razão completo com justificativa e citação por-família.

Escalonadores de lista baseados em posto

Três escalonadores de lista conscientes-de-heterogeneidade compartilham uma forma de duas-fases: uma fase de priorização estática classifica cada tarefa sobre o DAG de precedência a partir de tempos-de-execução médios e custos de comunicação, e uma fase de seleção-de-processador vincula cada tarefa em ordem de prioridade por meio da regra de fim-mais-cedo do construtor serial. Os tempos-de-execução por-recurso vêm dos modos de execução declarados de uma tarefa moldável, e uma tarefa rígida degenera para a especialização homogênea documentada.

Solver	Base de prioridade	Referência canônica
`heft`	posto ascendente	Topcuoglu, Hariri & Wu (2002)
`cpop`	posto ascendente-mais-descendente combinado, tarefas de caminho-crítico primeiro	Topcuoglu, Hariri & Wu (2002)
`peft`	posto de tabela-de-custo-otimista	Arabnejad & Barbosa (2014)

Regras de mapeamento do conjunto-pronto

Três regras de mapeamento em lote pontuam cada tarefa pronta-em-precedência por sua conclusão mais cedo sobre suas vinculações candidatas, e então mapeiam uma tarefa por passo. As regras publicadas mapeiam um lote de tarefas independentes; aqui o lote é o conjunto pronto-em-precedência, de modo que as regras se estendem a cargas-de-trabalho dependentes e se reduzem ao comportamento publicado em instâncias de tarefas-independentes. Essas famílias deixaram o livro-razão aposentado para registro de primeira-classe uma vez que os TaskSpec.modes moldáveis tornaram representável sua matriz de conclusão-esperada por-máquina.

Solver	Base de prioridade	Referência canônica
`min-min`	menor melhor-conclusão a seguir	Ibarra & Kim (1977); Braun et al. (2001)
`max-min`	maior melhor-conclusão a seguir	Ibarra & Kim (1977); Braun et al. (2001)
`sufferage`	maior lacuna de conclusão segunda-melhor-menos-melhor a seguir	Maheswaran et al. (1999)

Linhas-base de guloso-iterado, tabu, e RCPSP

Três métodos publicados de solução-única buscam o espaço de ordem-de-tarefas seguro-em-precedência, cada um projetando seu mecanismo canônico sobre a costura compartilhada de ordem-execução. serial-sgs-justification é o primeiro solver de escalonamento-de-projeto com-restrição-de-recursos (RCPSP) da plataforma.

Solver	Mecanismo	Referência canônica
`iterated-greedy-rs`	semente NEH com um laço de destruição-reconstrução e aceitação de temperatura-fixa	Ruiz & Stutzle (2007)
`critical-path-tabu`	busca tabu avançada sobre movimentos de bloco de caminho-crítico	Nowicki & Smutnicki (2005)
`serial-sgs-justification`	decode de esquema-de-geração-de-escalonamento serial com dupla justificação direita-depois-esquerda	Valls, Ballestin & Quintanilla (2005)

Linhas-base metaheurísticas

As linhas-base metaheurísticas semeadas representativas compartilham os mesmos operadores de factibilidade, reparo, pontuação, e busca-local, cada uma com sua referência canônica: algoritmo genético (Holland 1975), recozimento simulado (Kirkpatrick et al. 1983), colônia de formigas (Dorigo, Maniezzo & Colorni 1996), enxame de partículas (Kennedy & Eberhart 1995), e evolução diferencial (Storn & Price 1997).

Solvers de adaptador codificado

A otimização por enxame de partículas com aprendizado abrangente e a evolução diferencial adaptativa por histórico-de-sucessos buscam um vetor contínuo de valores-reais com um componente por tarefa. Cada núcleo é genérico sobre um objetivo contínuo, de modo que seu comportamento de convergência é validado diretamente sobre um benchmark contínuo, e é vinculado ao problema de escalonamento por meio de um adaptador de codificação que decodifica um vetor em uma ordem de tarefas factível-em-precedência.

Solver	Inspiração	Adaptação	Codificação
`clpso`	Enxame de partículas com aprendizado abrangente (Liang et al. 2006)	Enxame contínuo decodificado a uma ordem de precedência	random-key
`d-clpso`	Enxame de partículas com aprendizado abrangente (Liang et al. 2006)	Adaptador discreto sobre o decode de menor-valor-de-posição	spv
`lshade`	DE adaptativa por histórico-de-sucessos com redução linear de população (Tanabe & Fukunaga 2014)	Evolução diferencial contínua decodificada a uma ordem de precedência	random-key
`d-lshade`	DE adaptativa por histórico-de-sucessos com redução linear de população (Tanabe & Fukunaga 2014)	Adaptador discreto sobre o arredondamento por posto-inteiro	rounding

CLPSO aprende cada dimensão de um exemplar de aprendizado-abrangente em vez de um único melhor-global, de modo que a melhor aptidão do enxame melhora monotonicamente sobre uma bacia unimodal. L-SHADE adapta as memórias de cruzamento e fator-de-escala de seu histórico de sucessos e encolhe a população linearmente até um mínimo de quatro indivíduos. Ambos reportam uma trajetória de convergência, e um teste de correção-de-convergência falha quando a trajetória observada contradiz o comportamento de referência publicado, separado das verificações de reprodutibilidade-de-semente.

Esses solvers são otimizadores contínuos adaptados a um domínio discreto por meio de uma ponte de codificação; eles não são reimplementações exatas de nenhum código anterior, e nenhuma afirmação de desempenho é feita antes de as campanhas comparativas rodarem.

Adaptadores de codificação

Cada codificação contínua-para-discreta nomeada é um adaptador distinto com uma política de reparo explícita, de modo que nenhuma codificação é dobrada silenciosamente em um decodificador genérico. Cada adaptador repara sua ordem decodificada em uma ordem factível-em-precedência e registra se o reparo disparou.

Adaptador	Transferência	Regra de decode	Estado de citação
`random-key`	identidade	ordenar por chave limitada	canônica (Bean 1994)
`spv`	identidade	menor valor de posição	canônica (Tasgetiren et al. 2007)
`rounding`	identidade	slots de posto inteiro	não-aplicável
`sigmoid`	sigmoide em S	sorteio probabilístico ponderado	canônica (Kennedy & Eberhart 1997)
`v-shaped`	magnitude em V	sorteio probabilístico ponderado	canônica (Mirjalili & Lewis 2013)
`tanh`	tangente hiperbólica deslocada	sorteio probabilístico ponderado	canônica (Mirjalili & Lewis 2013)

Os decodes determinísticos (random-key, spv, rounding) ignoram a fonte aleatória; os decodes de função-de-transferência consomem uma fonte semeada e se replicam sob uma semente fixa. O adaptador rounding registra citação-não-aplicável porque o arredondamento por posto ao-inteiro-mais-próximo é uma discretização genérica sem uma única origem seminal canônica.

Conjunto de competidores diversificado

O conjunto de competidores diversificado amplia a comparação além das linhas-base representativas com um pequeno punhado de pares recentes e fortes, cada um um solver nomeado com sua referência de texto-completo projetada sobre o espaço de decisão de somente-ordem, em vez de uma lista volumosa de linhas-base clássicas. Um par permanece no campo público apenas quando supera uma barra de força-de-veículo: publicado dentro da janela de vigência pós-2020 em um veículo indexado e revisado-por-pares, com o nível do veículo registrado para que um leitor o pondere por evidência em vez de por prestígio.

Solver	Mecanismo	Veículo	Citação
`epso`	Enxame com inicialização-enviesada-por-carga com coleta de caminhos	Electronics (MDPI), 2023 — indexado	Anbarkhan & Rakrouki (2023)
`adpso`	Busca por enxame com inércia descendente adaptativa-ao-sucesso	Sensors (MDPI), 2022 — indexado	Nabi et al. (2022)
`ccgp`	Coevolução cooperativa de árvores de regras-de-prioridade	Computers & Operations Research (Elsevier), 2024 — OR de primeiro-nível	Zaki et al. (2024)

Cada par carrega forças, ressalvas, e uma classe de evidência de linha-base-executável em seus metadados para que o recomendador possa explicar por que um solver se ajusta a um contexto. Um conjunto mais amplo de linhas-base clássicas — algoritmos genéticos de chave-inteira, chave-aleatória-enviesada, e estimação-de-distribuição, e buscas de grande-vizinhança, gulosas-iteradas, tabu, vizinhança-variável, e meméticas — permanece registrado para comparação interna mas é mantido fora do campo público, já que as heurísticas representativas já carregam seu sinal de mecanismo. Nenhuma afirmação de desempenho é feita antes de as campanhas comparativas rodarem.

Âncora de vigência

O conjunto de competidores e trabalho-relacionado é posicionado contra o trabalho da década-atual por meio de uma âncora de vigência pós-2020: Karimi-Mamaghan, Mohammadi, Pasdeloup, e Meyer (2023, aprender a selecionar operadores via Q-learning integrado em greedy iterado para o flowshop de permutação, European Journal of Operational Research 304(3):1296-1330, doi:10.1016/j.ejor.2022.03.054) e o algoritmo evolutivo multi-objetivo colaborativo dirigido-por-indicador de 2024 IEEE Transactions on Evolutionary Computation para o escalonamento de grupos de flowshop distribuído (doi:10.1109/TEVC.2023.3339558).

Competidores multi-objetivo

Dois competidores de Pareto evoluem uma população de ordens de tarefas seguras-em-precedência sobre um vetor multi-objetivo, distintos da superfície de muitos-objetivos nsga3. nsga2 é o competidor baseado-em-dominância — ordenação não-dominada rápida com desempate por distância-de-aglomeração — e moead é o contrapeso baseado-em-decomposição, dividindo o problema em subproblemas escalares de Tchebycheff ao longo de uma grade estruturada de pesos no simplex e substituindo os incumbentes através da vizinhança de peso-mais-próximo de cada subproblema.

Solver	Mecanismo	Referência canônica
`nsga2`	Pareto baseado-em-dominância: ordenação não-dominada rápida, distância-de-aglomeração	Deb et al. (2002)
`moead`	Pareto baseado-em-decomposição: escalarização de Tchebycheff sobre uma grade de pesos no simplex	Zhang & Li (2007)

Competidor de muitos-objetivos

nsga3 é o competidor de muitos-objetivos nomeado (Deb & Jain 2014, doi:10.1109/TEVC.2013.2281535), distinto da superfície de competidor bi-objetivo. Mantém uma população sobre ordens de tarefas seguras-em-precedência, avalia cada ordem em um vetor multi-objetivo (makespan, atraso, e equidade de carga), e sobrevive a cada geração por uma seleção de nichamento-por-pontos-de-referência sobre os pontos de referência estruturados de Das & Dennis no simplex unitário. O design de pontos-de-referência, a associação de soluções à sua direção de referência mais próxima, e a seleção de contagem-de-nicho são a assinatura algorítmica; o solver declara a capacidade many-objective junto a multi-objective para que uma campanha possa selecioná-lo explicitamente.

Correspondência de capacidades

SolverRegistry.select corresponde solvers por capacidade, restrição, e objetivo. Os requisitos de capacidade e restrição são ambos expressos como rótulos de capacidade e correspondidos como uma conjunção: uma campanha de muitos-objetivos que também precisa de consciência-de-deadline passa required_capabilities=(SolverCapability.MANY_OBJECTIVE,) e required_constraints=(SolverCapability.DEADLINE_AWARE,), e o registro retorna apenas solvers que declaram ambas. Um filtro objective restringe ainda mais o resultado a solvers que declaram suporte para aquele objetivo nomeado.

Família NDSO

A família NDSO é a família de solver de codificação-nativa do registro: busca diretamente sobre escalonamentos factíveis. Cada escalonamento que produz é factível por construção (um construtor de validade-por-design constrói uma ordem respeitosa-da-precedência a cada passo), de modo que a família carrega a codificação native e nunca executa um passo de codificar/decodificar nem uma passada de reparo. A família compõe um pequeno conjunto de mecanismos nomeados:

Matriz de confiança — um armazenamento esparso de confiança aprendida por célula (posição, tarefa), atualizado conforme os melhores escalonamentos reforçam suas células.
Votação ponderada-por-confiança — sintetiza o escalonamento elite votando através da população ponderada pela Matriz de confiança; a variante rápida usa em vez disso um voto majoritário não-ponderado.
Escalonamentos de quantidade-e-qualidade — o escalonamento de Quantidade define quanto um candidato muda; o escalonamento de Qualidade define de qual fonte de orientação ele aprende.
Orientação de três-fontes — um candidato aprende da elite sintetizada (exploração- produtiva), um par (diversidade), ou uma fonte de conhecimento-desaparecido (exploração radical).
Coeficiente adaptativo unificado — um escalonamento não-linear desloca a família da exploração para a exploração-produtiva e impulsiona tanto a sensibilidade quanto o foco-de-aprendizado; a variante rápida o fixa a um valor fixo.

Variante	Composição
`ndso-core`	Votação ponderada-por-confiança, coeficiente adaptativo, orientação de três-fontes
`ndso-fast`	síntese por voto-majoritário, coeficiente fixo, fonte de orientação única
`ndso-summit`	Conselho Inter-Enxame coordenando vários enxames com síntese abrangente

Conselho Inter-Enxame

A variante ndso-summit é a composição de qualidade: roda vários enxames em paralelo e os coordena através de um conselho. Cada enxame compõe os mecanismos núcleo sobre sua própria população; o conselho Inter-Enxame mantém esses enxames trabalhando como uma única busca em vez de várias execuções isoladas e sintetiza seus resultados em uma única elite abrangente — o escalonamento por trás do qual toda a cúpula se posiciona. O conselho é o que distingue a variante à primeira vista: ndso-core e ndso-fast buscam cada um com uma população, enquanto ndso-summit é a composição construída para busca multi-enxame coordenada.

O comportamento de coordenação do conselho e os diagnósticos por-enxame são configuráveis, e cada escalonamento que produz permanece factível por construção.

Mapa de ablação

O mapa de ablação enumera uma configuração isolante por mecanismo nomeado para que a análise posterior possa atribuir a contribuição de cada mecanismo. As entradas intra-enxame cada uma desabilitam um interruptor núcleo — a Matriz de confiança, a Votação ponderada-por-confiança, o escalonamento de Quantidade, o escalonamento de Qualidade, a estrutura de orientação multi-fonte, a fonte par, a fonte de conhecimento-desaparecido, e o coeficiente adaptativo. As entradas de coordenação cada uma desabilitam um parâmetro do conselho — a coordenação Intra- versus Inter-Enxame e a síntese abrangente. Cada entrada declara o piso de contagem-de-execuções no qual a camada de análise amostra sua comparação e nomeia os testes estatísticos que essa camada aplica (um teste de significância não-paramétrico pareado, um post-hoc de posto-médio de Friedman com uma correção de comparação-múltipla de Holm, e um tamanho-de-efeito de delta-de-Cliff). A comparação amostrada é materializada pelo motor de experimentos posterior; o runner em-processo prova que cada isolamento é factível.

Um filtro de exportação escalonado governa quais mecanismos um escopo de relatório pode expor: o escopo base expõe apenas os mecanismos fundacionais, e um gate fail-closed levanta em vez de vazar um mecanismo mais-rápido ou de maior-qualidade sob um escopo base, de modo que os escopos fundacional e rápido não podem expor os mecanismos somente-de-conselho. Os diagnósticos de convergência — diversidade de população, entropia da Matriz-de-confiança, razão de exploração, carga de recursos, temporização, e o traço de melhoria — são opcionais e não adicionam sobrecarga quando desabilitados, e o conselho exporta um manifesto JSON que a camada de análise ingere sem importar nenhum tipo de solver.

Adaptadores exatos

Os adaptadores exatos tomam uma de duas formas. Os adaptadores de backend-opcional declaram uma dependência opcional e verificam sua raiz de importação em runtime; se o backend está ausente eles levantam MissingOptionalDependencyError com o nome do extra, o propósito, a flag comercial, e a nota de licenciamento em vez de importar uma dependência pesada durante a importação do pacote. Os adaptadores nativo-limitados não carregam dependência de terceiros e resolvem instâncias pequenas exatamente por sua própria busca limitada — enumerando ordens factíveis-em-precedência em um caso, branch-and-bound de diagrama-de-decisão no outro —, levantando UnsupportedCapabilityError quando a instância excede a contagem de tarefas suportada.

Solver	Método	Backend	Extra	Comercial
`ortools-cp-sat`	CP-SAT	`ortools`	`exact`	não
`pulp-milp`	MILP / MIP	`pulp`	`exact`	não
`branch-and-bound`	branch-and-bound / cut	`ortools`	`exact`	não
`logic-based-benders-decomposition`	decomposição de Benders baseada-em-lógica	`pulp`	`exact`	não
`exhaustive-enumeration`	enumeração exaustiva	nativo (sem backend)	—	não
`decision-diagram-sequencing`	branch-and-bound de diagrama-de-decisão	nativo (sem backend)	—	não
`gurobi-exact`	MILP / MIP	`gurobipy`	`exact-commercial`	sim

O punhado exato público é o conjunto aberto representativo — CP-SAT, MILP, branch-and-bound, e decomposição de Benders baseada-em-lógica — mais os dois solvers nativos limitados e o invólucro de Gurobi genuinamente invocante. O invólucro comercial de Gurobi permanece atrás do extra exact-commercial, nunca é empacotado, e carrega uma nota de licenciamento explícita; há uma licença acadêmica disponível. Um pool mais amplo de backends opcionais permanece registrado para uso interno.

Camada de seleção e aprendizado

A camada de seleção classifica solvers para um problema sem nunca afirmar um solver globalmente melhor. Consome um registro SelectionFeatures — difficulty, heterogeneity, objective_conflict, uncertainty, dynamism, e solver_sensitivity — e metadados públicos de solver, e retorna linhas SolverRecommendation classificadas. Cada recomendação carrega um RecommendationSource (metadata ou learned-model), um ConfidenceLabel, e notas de limitação explícitas, de modo que uma recomendação de metadados nunca é confundida com uma aprendida.

FEATURE_ORIGIN rastreia cada característica até a métrica de caracterização de benchmark que lê; o consumidor mapeia as métricas de caracterização de dispatchatlas.bench no contrato de características, de modo que dispatchatlas.solve ainda importa apenas dispatchatlas.core. Dois seletores são enviados: RuleBasedSelector classifica a partir de capacidades declaradas e caracterização apenas (uma recomendação de metadados), e SupervisedSelector classifica a partir de um corpus rotulado por um modelo determinístico de vizinho-mais-próximo ponderado-por-distância (uma recomendação de modelo-aprendido).

O seletor supervisionado reporta generalização em-reserva, nunca ajuste de treinamento. O protocolo de validação-cruzada particiona um corpus rotulado de modo que nenhuma instância, nenhuma família de benchmark, e nenhum registro de caracterização apareça em ambas as partições de treinamento e de teste (partition_by_families, leave_one_family_out, leakage_report), treina sobre as famílias restantes, e pontua a família em-reserva (held_out_generalization, cross_validate). Sua confiança sobe acima de somente-metadados apenas quando uma avaliação em-reserva livre-de-vazamento a respalda.

learning_interface_catalog() registra sete interfaces de aprendizado e híbridas nomeadas — seleção de algoritmo supervisionada, busca assistida-por-substituto, ganchos de aprendizado-por-reforço, hiper-heurísticas, reparo guiado-por-política, inicialização aprendida, e uma linha-base somente-de-benchmark. Cada uma declara uma LearningEvidencePolicy (dados de treinamento, controles de vazamento, reprodutibilidade, classe de evidência, elegibilidade de nível-de-evidência). Duas estão implementadas aqui; as outras cinco são interfaces diferidas registradas cuja realização está condicionada às campanhas comparativas que produzem dados de treinamento. Os backends de estimador pesados permanecem atrás do extra opcional learning e são sondados por raiz de importação, nunca importados durante a importação do pacote; a ausência levanta MissingOptionalDependencyError enquanto o fallback determinístico permanece disponível.

Salvaguardas de desempenho

A pontuação em lote é explícita por meio de BatchScoringProfile. O kernel atual usa um fallback de biblioteca-padrão e registra esse fallback nos diagnósticos. Isso mantém a API pronta para kernels vetorizados ou acelerados enquanto preserva um caminho testado e portável.