Solver-System

dispatchatlas.solve besitzt die Solver-Metadaten, die Auswahl, die Zeitplan-Konstruktion, die Reparatur, die Leistungs-Schutzvorrichtungen, die Referenzfamilien, die optionalen Adapter, und die NDSO-Familie.

Das Paket importiert nur dispatchatlas.core. Benchmark-Smoke-Integrationstests leben in tests/solve/, sodass das Laufzeitpaket nicht von konkreten Benchmark-Generatoren abhängt.

Ausführbares Beispiel: examples/compare_solvers.py plant eine Solver-Kohorte parallel und verankert sie mit einem bewiesenen exakten Optimum.

Registry

SolverRegistry speichert zustandslose Solver-Fabriken mit reichen Metadaten:

Fähigkeits-Tags wie capacity-aware, precedence-aware, repair, local-search, und ndso
unterstützte Ziele wie makespan, energy, und cost
deklarierte Constraints, ausgedrückt durch Fähigkeits-Tags
Standard-Stoppkriterien
deterministisches oder seed-stochastisches Wiederholungsverhalten
Lösungs-Encoding (permutation, mapping, assignment, oder native)
optionale Abhängigkeitsdeklarationen und kommerzielle-Backend-Offenlegung
Belegstufen-Sichtbarkeit für gestufte Exporte
eine kanonische Zitation, oder eine explizite Zitation-nicht-anwendbar-Begründung

Jede benannte Solver-Familie trägt eine Zitation, die schließend fehlschlägt: eine Familie mit einem kanonischen seminalen Ursprung, die ihre Referenz auslässt, kann nicht konstruiert werden, und eine Familie ohne einen einzigen kanonischen Ursprung zeichnet den Grund auf, statt einen zu erfinden.

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

Registry-Katalog

Jeder Solver in der Registry, in einer einzigen sortierbaren, durchsuchbaren Tabelle. Die Tabelle und ihre Summen werden aus default_solver_registry() generiert, sodass die Zahlen unten aus den Zeilen selbst zählbar sind. Ein Fähigkeits-Abdeckungs-Diagramm über der Tabelle fasst zusammen, wie viele Registry-Solver jede deklarierte Fähigkeit anbieten.

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.

Solver-Anwendbarkeit

Welche Solver für welche Benchmark-Familie gelten, gelesen aus der Anwendbarkeitsmatrix. Jede Zelle ist deklarierte Anwendbarkeit, niemals eine Leistungsaussage: verifizierte Zellen nennen eine benannte öffentliche Kampagne, Zitation oder einen Test, und approximative Zellen werden aus den deklarierten Solver-Fähigkeiten und den Merkmalen der Benchmark-Familie abgeleitet. Die folgende Tabelle wird aus dem öffentlichen Anwendbarkeitspaket generiert, sodass ihre Status-Summen aus den Zeilen selbst zählbar sind.

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.

Dispatch-Baselines

Die gebündelten deterministischen Baselines sind konstruktive Listen-Scheduling-Solver. Jeder steuert die Aufgaben-Reihenfolge; der serielle Konstruktor honoriert die deklarierten Ressourcen-Anforderungen jeder Aufgabe und weist jede Aufgabe ihrer frühest-verfügbaren geforderten Ressource zu. Jede Familie nennt ihre kanonische seminale Referenz.

Solver	Prioritätsbasis	Kanonische Referenz
`earliest-start`	topologische Eingabereihenfolge	nicht anwendbar (Identitäts-Baseline)
`shortest-processing-time`	kürzeste Aufgabendauer zuerst	Smith (1956)
`longest-processing-time`	längste Aufgabendauer zuerst	Graham (1969)
`earliest-deadline`	frühester Deadline zuerst (deadline-bewusst)	Jackson (1955)
`earliest-finish-time`	frühestes erreichbares Ende zuerst	Topcuoglu et al. (2002)
`minimum-slack`	kleinster Zeitplan-Spielraum zuerst	Conway, Maxwell & Miller (1967)
`greedy-completion`	frühester zustands-bewusster Abschluss zuerst	Graham (1966)
`apparent-tardiness-cost`	höchster Index der scheinbaren Verspätungskosten zuerst (deadline-bewusst)	Vepsalainen & Morton (1987)

Zurückgezogene Dispatch-Familien

Ressourcen-Mapping-Familien werden explizit aufgezeichnet, statt stillschweigend in einen generischen Typ gefaltet zu werden. Sie steuern die Ressourcen-Zuweisung, nicht die Aufgaben-Reihenfolge, und der nachfrage-respektierende serielle Konstruktor stellt unter dem aktuellen Kern-Problemmodell keine freie Mapping-Entscheidung bereit:

Ressourcen-Mapping (olb, met, mct, round-robin, load-balanced): der Konstruktor weist jede Aufgabe ihrer deklarierten Ressource zu, sodass diese Heuristiken keinen realisierbaren Freiheitsgrad haben; ausführungszeit-bewusstes Mapping gehört zur Ziele-und-Constraints-Arbeit, nicht zu dieser Grundlage.
Typ-Tags fehlen (type-aware, domain-aware): erfordern Aufgabentyp- und Ressourcentyp-Tags, die das aktuelle Kern-Problemmodell nicht trägt.

retired_dispatching_families() gibt das vollständige Hauptbuch mit Begründung und Zitation pro-Familie zurück.

Rang-basierte Listen-Scheduler

Drei heterogenitäts-bewusste Listen-Scheduler teilen eine zweiphasige Form: eine statische Priorisierungsphase rankt jede Aufgabe über den Präzedenz-DAG aus mittleren Ausführungszeiten und Kommunikationskosten, und eine Prozessor-Auswahlphase bindet jede Aufgabe in Prioritätsreihenfolge durch die Frühestes-Ende-Regel des seriellen Konstruktors. Pro-Ressource-Ausführungszeiten stammen aus den deklarierten Ausführungsmodi einer formbaren Aufgabe, und eine starre Aufgabe degeneriert zur dokumentierten homogenen Spezialisierung.

Solver	Prioritätsbasis	Kanonische Referenz
`heft`	Aufwärtsrang	Topcuoglu, Hariri & Wu (2002)
`cpop`	kombinierter Aufwärts- und Abwärtsrang, Kritischer-Pfad-Aufgaben zuerst	Topcuoglu, Hariri & Wu (2002)
`peft`	Rang der optimistischen Kostentabelle	Arabnejad & Barbosa (2014)

Ready-Set-Mapping-Regeln

Drei Batch-Mapping-Regeln bewerten jede präzedenz-bereite Aufgabe nach ihrem frühesten Abschluss über ihre Kandidaten-Bindungen und mappen dann eine Aufgabe pro Schritt. Die veröffentlichten Regeln mappen einen Batch unabhängiger Aufgaben; hier ist der Batch die präzedenz-bereite Menge, sodass die Regeln sich auf abhängige Workloads erweitern und sich auf Instanzen mit unabhängigen Aufgaben auf das veröffentlichte Verhalten reduzieren. Diese Familien verließen das Hauptbuch der zurückgezogenen Familien für die erstklassige Registrierung, sobald formbare TaskSpec.modes ihre erwartete Abschlussmatrix pro Maschine darstellbar machten.

Solver	Prioritätsbasis	Kanonische Referenz
`min-min`	kleinster bester Abschluss als Nächstes	Ibarra & Kim (1977); Braun et al. (2001)
`max-min`	größter bester Abschluss als Nächstes	Ibarra & Kim (1977); Braun et al. (2001)
`sufferage`	größte Zweitbester-minus-Bester-Abschluss-Lücke als Nächstes	Maheswaran et al. (1999)

Iterated-Greedy-, Tabu- und RCPSP-Baselines

Drei veröffentlichte Einzellösungs-Methoden durchsuchen den präzedenz-sicheren Aufgaben-Reihenfolge-Raum, wobei jede ihren kanonischen Mechanismus auf die gemeinsame Reihenfolge-Lauf-Naht projiziert. serial-sgs-justification ist der erste Solver der Plattform für ressourcenbeschränkte Projektplanung (RCPSP).

Solver	Mechanismus	Kanonische Referenz
`iterated-greedy-rs`	NEH-Seed mit einer Zerstörungs-Rekonstruktions-Schleife und Festtemperatur-Akzeptanz	Ruiz & Stutzle (2007)
`critical-path-tabu`	fortgeschrittene Tabu-Suche über Kritischer-Pfad-Blockzüge	Nowicki & Smutnicki (2005)
`serial-sgs-justification`	serielle Schedule-Generation-Scheme-Dekodierung mit doppelter Rechts-dann-Links-Justierung	Valls, Ballestin & Quintanilla (2005)

Metaheuristische Baselines

Repräsentative seed-gesetzte metaheuristische Baselines teilen dieselben Machbarkeits-, Reparatur-, Bewertungs-, und Lokalsuche-Operatoren, jede mit ihrer kanonischen Referenz: genetic algorithm (Holland 1975), simulated annealing (Kirkpatrick et al. 1983), ant colony (Dorigo, Maniezzo & Colorni 1996), particle swarm (Kennedy & Eberhart 1995), und differential evolution (Storn & Price 1997).

Encoded-Adapter-Solver

Die comprehensive learning particle swarm optimization und die success-history adaptive differential evolution durchsuchen einen kontinuierlichen, reellwertigen Vektor mit einer Komponente pro Aufgabe. Jeder Kern ist generisch über ein kontinuierliches Ziel, sodass sein Konvergenzverhalten direkt auf einem kontinuierlichen Benchmark validiert wird, und ist über einen Encoding-Adapter, der einen Vektor in eine präzedenz-machbare Aufgaben-Reihenfolge dekodiert, an das Scheduling-Problem gebunden.

Solver	Inspiration	Anpassung	Encoding
`clpso`	Comprehensive learning particle swarm (Liang et al. 2006)	Kontinuierlicher Schwarm dekodiert zu einer Präzedenz-Reihenfolge	random-key
`d-clpso`	Comprehensive learning particle swarm (Liang et al. 2006)	Diskreter Adapter über die smallest-position-value-Dekodierung	spv
`lshade`	Success-history adaptive DE mit linearer Populationsreduktion (Tanabe & Fukunaga 2014)	Kontinuierliche differential evolution dekodiert zu einer Präzedenz-Reihenfolge	random-key
`d-lshade`	Success-history adaptive DE mit linearer Populationsreduktion (Tanabe & Fukunaga 2014)	Diskreter Adapter über Ganzzahl-Rang-Rundung	rounding

CLPSO lernt jede Dimension aus einem comprehensive-learning-Exemplar statt aus einem einzigen Global-Besten, sodass die beste Fitness des Schwarms monoton über ein unimodales Becken verbessert. L-SHADE passt die Crossover- und Skalierungsfaktor-Speicher aus seiner Erfolgshistorie an und schrumpft die Population linear auf ein Minimum von vier Individuen. Beide berichten eine Konvergenztrajektorie, und ein Konvergenz-Korrektheits-Test schlägt fehl, wenn die beobachtete Trajektorie dem veröffentlichten Referenzverhalten widerspricht, getrennt von den Seed-Reproduzierbarkeits-Prüfungen.

Diese Solver sind kontinuierliche Optimierer, die über eine Encoding-Brücke an eine diskrete Domäne angepasst sind; sie sind keine exakten Neuimplementierungen irgendeines vorherigen Codes, und es wird keine Leistungsaussage gemacht, bevor die vergleichenden Kampagnen laufen.

Encoding-Adapter

Jedes benannte kontinuierlich-zu-diskrete Encoding ist ein eigener Adapter mit einer expliziten Reparatur-Politik, sodass kein Encoding stillschweigend in einen generischen Dekoder gefaltet wird. Jeder Adapter repariert seine dekodierte Reihenfolge in eine präzedenz-machbare Reihenfolge und zeichnet auf, ob die Reparatur ausgelöst wurde.

Adapter	Transfer	Dekodierregel	Zitationsstatus
`random-key`	Identität	nach geklammertem Schlüssel sortieren	kanonisch (Bean 1994)
`spv`	Identität	smallest position value	kanonisch (Tasgetiren et al. 2007)
`rounding`	Identität	Ganzzahl-Rang-Slots	nicht-anwendbar
`sigmoid`	S-förmiges Sigmoid	gewichtete probabilistische Ziehung	kanonisch (Kennedy & Eberhart 1997)
`v-shaped`	V-förmige Magnitude	gewichtete probabilistische Ziehung	kanonisch (Mirjalili & Lewis 2013)
`tanh`	verschobener hyperbolischer Tangens	gewichtete probabilistische Ziehung	kanonisch (Mirjalili & Lewis 2013)

Die deterministischen Dekodierungen (random-key, spv, rounding) ignorieren die Zufallsquelle; die Transferfunktions-Dekodierungen konsumieren eine seed-gesetzte Quelle und wiederholen unter einem festen Seed. Der rounding-Adapter zeichnet Zitation-nicht-anwendbar auf, weil die Nächste-Ganzzahl-Rang-Rundung eine generische Diskretisierung ohne einen einzigen kanonischen seminalen Ursprung ist.

Diversifizierte Konkurrenten-Menge

Die diversifizierte Konkurrenten-Menge erweitert den Vergleich über die repräsentativen Baselines hinaus mit einer kleinen Handvoll aktueller, starker Peers, jeder ein benannter Solver mit seiner Volltext-Referenz, projiziert auf den nur-Reihenfolge-Entscheidungsraum, statt einer sperrigen Liste klassischer Baselines. Ein Peer bleibt nur dann auf dem öffentlichen Feld, wenn er eine Venue-Stärke-Latte überspringt: veröffentlicht innerhalb des Post-2020-Aktualitätsfensters in einer indexierten, peer-reviewten Venue, mit der aufgezeichneten Venue-Stufe, sodass ein Leser sie nach Evidenz statt nach Prestige gewichtet.

Solver	Mechanismus	Venue	Zitation
`epso`	Workload-bias-initialisierter Schwarm mit Pfad-Sammlung	Electronics (MDPI), 2023 — indexiert	Anbarkhan & Rakrouki (2023)
`adpso`	Schwarmsuche mit erfolgs-adaptiver absteigender Trägheit	Sensors (MDPI), 2022 — indexiert	Nabi et al. (2022)
`ccgp`	Kooperative Koevolution von Prioritätsregel-Bäumen	Computers & Operations Research (Elsevier), 2024 — Top-Tier-OR	Zaki et al. (2024)

Jeder Peer trägt Stärken, Vorbehalte, und eine ausführbare-Baseline-Belegklasse in seinen Metadaten, sodass der Empfehler erklären kann, warum ein Solver in einen Kontext passt. Eine breitere Menge klassischer Baselines — Ganzzahl-Schlüssel-, biased-random-key-, und estimation-of-distribution-genetic-algorithms, und large-neighborhood-, iterated-greedy-, tabu-, variable-neighborhood-, und memetic-Suchen — bleibt für internen Vergleich registriert, wird aber vom öffentlichen Feld ferngehalten, da die repräsentativen Heuristiken ihr Mechanismus-Signal bereits tragen. Es wird keine Leistungsaussage gemacht, bevor die vergleichenden Kampagnen laufen.

Aktualitäts-Anker

Die Konkurrenten- und verwandte-Arbeit-Menge wird gegen die Arbeit der aktuellen Dekade positioniert durch einen Post-2020-Aktualitätsanker: Karimi-Mamaghan, Mohammadi, Pasdeloup, und Meyer (2023, Lernen, Operatoren auszuwählen via Q-Learning integriert in iterated greedy für den permutation flowshop, European Journal of Operational Research 304(3):1296-1330, doi:10.1016/j.ejor.2022.03.054) und der indikator-getriebene kollaborative multi-objektive evolutionäre Algorithmus der 2024 IEEE Transactions on Evolutionary Computation für distributed flowshop group scheduling (doi:10.1109/TEVC.2023.3339558).

Multi-Ziel-Konkurrenten

Zwei Pareto-Konkurrenten entwickeln eine Population präzedenz-sicherer Aufgaben-Reihenfolgen auf einem multi-objektiven Vektor, getrennt von der Viel-Ziel-Oberfläche nsga3. nsga2 ist der dominanz-basierte Konkurrent — schnelle nichtdominierte Sortierung mit Crowding-Distance-Gleichstand-Auflösung — und moead ist das dekompositions-basierte Gegengewicht, das das Problem entlang eines strukturierten Simplex-Gewichtsgitters in skalare Tchebycheff-Teilprobleme aufteilt und amtierende Lösungen über die Nachbarschaft der nächsten Gewichte jedes Teilproblems ersetzt.

Solver	Mechanismus	Kanonische Referenz
`nsga2`	dominanz-basiertes Pareto: schnelle nichtdominierte Sortierung, Crowding-Distance	Deb et al. (2002)
`moead`	dekompositions-basiertes Pareto: Tchebycheff-Skalarisierung über einem Simplex-Gewichtsgitter	Zhang & Li (2007)

Viel-Ziel-Konkurrent

nsga3 ist der benannte Viel-Ziel-Konkurrent (Deb & Jain 2014, doi:10.1109/TEVC.2013.2281535), getrennt von der Bi-Ziel-Konkurrent-Oberfläche. Er erhält eine Population über präzedenz-sichere Aufgaben-Reihenfolgen, bewertet jede Reihenfolge auf einem multi-objektiven Vektor (makespan, Verspätung, und Lastfairness), und überlebt jede Generation durch eine Referenzpunkt-Nischenselektion über strukturierte Das-&-Dennis- Referenzpunkte auf dem Einheitssimplex. Das Referenzpunkt-Design, die Assoziation von Lösungen zu ihrer nächsten Referenzrichtung, und die Nischen-Zähl-Selektion sind die algorithmische Signatur; der Solver deklariert die Fähigkeit many-objective neben multi-objective, sodass eine Kampagne ihn explizit auswählen kann.

Fähigkeits-Matching

SolverRegistry.select matched Solver nach Fähigkeit, Constraint, und Ziel. Fähigkeits- und Constraint-Anforderungen werden beide als Fähigkeits-Tags ausgedrückt und als Konjunktion gematcht: eine Viel-Ziel-Kampagne, die auch Deadline-Bewusstheit braucht, übergibt required_capabilities=(SolverCapability.MANY_OBJECTIVE,) und required_constraints=(SolverCapability.DEADLINE_AWARE,), und die Registry gibt nur Solver zurück, die beide deklarieren. Ein objective-Filter schränkt das Ergebnis weiter auf Solver ein, die Unterstützung für dieses benannte Ziel deklarieren.

NDSO-Familie

Die NDSO-Familie ist die native-Encoding-Solver-Familie der Registry: sie durchsucht direkt über machbare Zeitpläne. Jeder Zeitplan, den sie produziert, ist machbar durch Konstruktion (ein gültig-per-Design-Konstruktor baut in jedem Schritt eine präzedenz-respektierende Reihenfolge), sodass die Familie das native-Encoding trägt und niemals einen Encode/Decode-Schritt oder einen Reparatur-Durchlauf ausführt. Die Familie komponiert eine kleine Menge benannter Mechanismen:

Confidence Matrix — ein dünnbesetzter Speicher gelernten Vertrauens pro (Position, Aufgabe)-Zelle, aktualisiert, wenn bessere Zeitpläne ihre Zellen verstärken.
Confidence-Weighted Voting — synthetisiert den Elite-Zeitplan durch Abstimmung über die von der Confidence Matrix gewichtete Population; die schnelle Variante nutzt stattdessen eine ungewichtete Mehrheitsabstimmung.
Quantity-and-Quality-Zeitpläne — der Quantity-Zeitplan setzt, wie viel ein Kandidat sich ändert; der Quality-Zeitplan setzt, von welcher Führungsquelle er lernt.
Drei-Quellen-Führung — ein Kandidat lernt von der synthetisierten Elite (Exploitation), einem Peer (Diversität), oder einer verschwundenen-Wissen-Quelle (radikale Exploration).
Vereinheitlichter adaptiver Koeffizient — ein nicht-linearer Zeitplan verschiebt die Familie von Exploration zu Exploitation und treibt sowohl die Sensitivität als auch den Lernfokus; die schnelle Variante fixiert ihn auf einen festen Wert.

Variante	Komposition
`ndso-core`	Confidence-Weighted Voting, adaptiver Koeffizient, Drei-Quellen-Führung
`ndso-fast`	Mehrheitsabstimmungs-Synthese, fester Koeffizient, einzelne Führungsquelle
`ndso-summit`	Inter-Schwarm-Rat, der mehrere Schwärme mit überspannender Synthese koordiniert

Inter-Schwarm-Rat

Die ndso-summit-Variante ist die Qualitäts-Komposition: sie führt mehrere Schwärme parallel aus und koordiniert sie über einen einzigen Rat. Jeder Schwarm komponiert die Kern-Mechanismen über seine eigene Population; der Inter-Schwarm-Rat hält diese Schwärme als eine Suche arbeitend statt als mehrere isolierte Läufe und synthetisiert ihre Ergebnisse in eine einzige überspannende Elite — den Zeitplan, hinter dem der ganze Gipfel steht. Der Rat ist das, was die Variante auf einen Blick unterscheidet: ndso-core und ndso-fast durchsuchen jeweils mit einer Population, während ndso-summit die für koordinierte Multi-Schwarm-Suche gebaute Komposition ist.

Das Koordinationsverhalten des Rates und die pro-Schwarm-Diagnostik sind konfigurierbar, und jeder Zeitplan, den er produziert, bleibt machbar durch Konstruktion.

Ablations-Karte

Die Ablations-Karte enumeriert eine isolierende Konfiguration pro benanntem Mechanismus, sodass die nachgelagerte Analyse den Beitrag jedes Mechanismus zuschreiben kann. Die Intra-Schwarm-Einträge deaktivieren jeweils einen Kern-Schalter — die Confidence Matrix, die Confidence-Weighted Voting, den Quantity-Zeitplan, den Quality-Zeitplan, die Multi-Quellen-Führungsstruktur, die Peer-Quelle, die verschwundene-Wissen-Quelle, und den adaptiven Koeffizienten. Die Koordinations-Einträge deaktivieren jeweils einen Rat-Parameter — die Intra- versus Inter-Schwarm-Koordination und die überspannende Synthese. Jeder Eintrag deklariert den Lauf-Zahl-Boden, an dem die Analyseschicht ihren Vergleich abtastet, und nennt die statistischen Tests, die jene Schicht anwendet (ein gepaarter nicht-parametrischer Signifikanztest, ein Friedman-Durchschnittsrang-Post-hoc mit einer Holm-Mehrfachvergleichs-Korrektur, und eine Cliff's-delta-Effektgröße). Der abgetastete Vergleich wird von der nachgelagerten Experiment-Engine materialisiert; der In-Prozess-Runner beweist, dass jede Isolation machbar ist.

Ein gestufter Export-Filter regelt, welche Mechanismen ein Berichts-Scope freilegen darf: der Basis-Scope legt nur die grundlegenden Mechanismen frei, und ein fail-closed-Gate hebt an, statt einen schnelleren oder höher-qualitativen Mechanismus unter einem Basis-Scope zu leaken, sodass die grundlegenden und schnellen Scopes die rat-eigenen Mechanismen nicht freilegen können. Die Konvergenz-Diagnostik — Populationsdiversität, Confidence-Matrix- Entropie, Explorationsverhältnis, Ressourcenlast, Timing, und die Verbesserungsspur — sind optional und fügen keinen Overhead hinzu, wenn deaktiviert, und der Rat exportiert ein JSON-Manifest, das die Analyseschicht ingestiert, ohne irgendeinen Solver-Typ zu importieren.

Exakte Adapter

Exakte Adapter nehmen eine von zwei Formen an. Optional-Backend-Adapter deklarieren eine optionale Abhängigkeit und prüfen ihre Import-Wurzel zur Laufzeit; wenn das Backend fehlt, heben sie MissingOptionalDependencyError mit dem Extra-Namen, dem Zweck, dem kommerziellen Flag, und der Lizenznotiz, statt eine schwergewichtige Abhängigkeit während des Paket-Imports zu importieren. Native-begrenzte Adapter tragen keine Drittpartei-Abhängigkeit und lösen kleine Instanzen exakt durch ihre eigene begrenzte Suche — Enumeration präzedenz-machbarer Reihenfolgen im einen Fall, Entscheidungsdiagramm-Branch-and-Bound im anderen — und heben UnsupportedCapabilityError, wenn die Instanz die unterstützte Aufgabenzahl überschreitet.

Solver	Methode	Backend	Extra	Kommerziell
`ortools-cp-sat`	CP-SAT	`ortools`	`exact`	nein
`pulp-milp`	MILP / MIP	`pulp`	`exact`	nein
`branch-and-bound`	branch-and-bound / cut	`ortools`	`exact`	nein
`logic-based-benders-decomposition`	logik-basierte Benders-Zerlegung	`pulp`	`exact`	nein
`exhaustive-enumeration`	erschöpfende Enumeration	nativ (kein Backend)	—	nein
`decision-diagram-sequencing`	Entscheidungsdiagramm-Branch-and-Bound	nativ (kein Backend)	—	nein
`gurobi-exact`	MILP / MIP	`gurobipy`	`exact-commercial`	ja

Die öffentliche exakte Handvoll ist die repräsentative offene Menge — CP-SAT, MILP, branch-and-bound, und logik-basierte Benders-Zerlegung — plus die zwei nativen begrenzten Solver und der echt aufrufende Gurobi-Wrapper. Der kommerzielle Gurobi-Wrapper bleibt hinter dem exact-commercial-Extra, wird nie gebündelt, und trägt eine explizite Lizenznotiz; eine akademische Lizenz ist verfügbar. Ein breiterer Pool optionaler Backends bleibt für internen Gebrauch registriert.

Auswahl- und Lernschicht

Die Auswahlschicht rankt Solver für ein Problem, ohne jemals einen global besten Solver zu behaupten. Sie konsumiert einen SelectionFeatures-Datensatz — difficulty, heterogeneity, objective_conflict, uncertainty, dynamism, und solver_sensitivity — und öffentliche Solver-Metadaten, und gibt gerankte SolverRecommendation-Zeilen zurück. Jede Empfehlung trägt eine RecommendationSource (metadata oder learned-model), ein ConfidenceLabel, und explizite Limitierungsnotizen, sodass eine Metadaten-Empfehlung nie mit einer gelernten verwechselt wird.

FEATURE_ORIGIN verfolgt jedes Feature zurück zur Benchmark-Charakterisierungsmetrik, die es liest; der Konsument mappt die Charakterisierungsmetriken von dispatchatlas.bench in den Feature-Vertrag, sodass dispatchatlas.solve weiterhin nur dispatchatlas.core importiert. Zwei Selektoren werden ausgeliefert: RuleBasedSelector rankt aus deklarierten Fähigkeiten und Charakterisierung allein (eine Metadaten-Empfehlung), und SupervisedSelector rankt aus einem etikettierten Korpus durch ein deterministisches distanz-gewichtetes Nächste-Nachbarn-Modell (eine gelernte-Modell-Empfehlung).

Der überwachte Selektor berichtet Holdout-Generalisierung, niemals Trainings-Fit. Das Kreuzvalidierungs-Protokoll partitioniert einen etikettierten Korpus so, dass keine Instanz, keine Benchmark-Familie, und kein Charakterisierungs-Datensatz in beiden Trainings- und Test-Partitionen erscheint (partition_by_families, leave_one_family_out, leakage_report), trainiert auf den verbleibenden Familien, und bewertet die zurückgehaltene Familie (held_out_generalization, cross_validate). Sein Vertrauen steigt nur dann über Metadaten-allein, wenn eine leckage-freie zurückgehaltene Evaluation es stützt.

learning_interface_catalog() registriert sieben benannte Lern- und Hybrid-Schnittstellen — überwachte Algorithmus-Auswahl, surrogat-unterstützte Suche, Reinforcement-Learning-Hooks, Hyper-Heuristiken, policy-geführte Reparatur, gelernte Initialisierung, und eine nur-Benchmark-Baseline. Jede deklariert eine LearningEvidencePolicy (Trainingsdaten, Leckage-Kontrollen, Reproduzierbarkeit, Belegklasse, Belegstufen-Eignung). Zwei sind hier implementiert; die anderen fünf sind registrierte aufgeschobene Schnittstellen, deren Realisierung von den vergleichenden Kampagnen abhängt, die Trainingsdaten produzieren. Schwere Estimator-Backends bleiben hinter dem optionalen learning-Extra und werden per Import-Wurzel sondiert, nie während des Paket-Imports importiert; Abwesenheit hebt MissingOptionalDependencyError, während der deterministische Rückfall verfügbar bleibt.

Leistungs-Schutzvorrichtungen

Die Batch-Bewertung ist explizit über BatchScoringProfile. Der aktuelle Kernel nutzt einen Standardbibliotheks-Rückfall und zeichnet diesen Rückfall in der Diagnostik auf. Dies hält die API bereit für vektorisierte oder beschleunigte Kernel, während ein getesteter, portabler Pfad bewahrt wird.