ソルバーシステム

dispatchatlas.solve はソルバーメタデータ、選択、スケジュール構築、修復、性能保護、ベースラインファミリ、オプションアダプタ、および NDSO ファミリを所有します。

このパッケージは dispatchatlas.core のみをインポートします。ベンチマークのスモーク統合テストは tests/solve/ にあるため、ランタイムパッケージは具体的なベンチマーク生成器に依存しません。

実行可能な例： examples/compare_solvers.py はソルバーのコホートを並列にスケジュールし、証明された厳密最適でそれを固定します。

レジストリ

SolverRegistry は豊富なメタデータを持つステートレスなソルバーファクトリを格納します：

capacity-aware、precedence-aware、repair、local-search、および ndso のような能力タグ
makespan、energy、および cost のような対応目的
能力タグを通じて表現される宣言された制約
既定の停止基準
決定論的またはシード化確率的なリプレイ挙動
解符号化（permutation、mapping、assignment、または native）
オプション依存宣言と商用バックエンド開示
段階的エクスポート用の証拠-層可視性
正準引用、または明示的な引用-非該当の根拠

各具名ソルバーファミリは閉じて失敗する引用を担います：正準的な先駆的起源を持ちながらその参照を省くファミリは構築できず、単一の正準起源を持たないファミリは一つを捏造する代わりに理由を記録します。

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

レジストリカタログ

レジストリ内のすべてのソルバーを、一つのソート可能・検索可能な表に。表とその合計は default_solver_registry() から生成されるため、下の数は行そのものから数えられます。表の上にある能力カバレッジグラフは、各宣言された能力をレジストリ内の何個のソルバーが告示するかを要約します。

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.

ソルバーの適用可能性

どのソルバーがどのベンチマークファミリーに適用されるかを、適用可能性マトリクスから読み取ります。各セルは宣言された適用可能性であり、性能の主張ではありません。検証済みセルは名前付きの公開キャンペーン、引用、またはテストを引用し、近似セルは宣言されたソルバー能力とベンチマークファミリーの特性から導出されます。以下の表は公開適用可能性バンドルから生成されるため、その状態合計は行そのものから数えられます。

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.

ディスパッチングベースライン

バンドルされた決定論的ベースラインは構築的リスト-スケジューリングソルバーです。各々がタスク順序を制御します；逐次構築器は各タスクの宣言された資源需要を尊重し、各タスクをその最早-利用可能な需要資源に割り当てます。各ファミリはその正準的な先駆的参照を命名します。

Solver	優先度基準	正準参照
`earliest-start`	トポロジカル入力順序	非該当（恒等ベースライン）
`shortest-processing-time`	最短タスク時間優先	Smith (1956)
`longest-processing-time`	最長タスク時間優先	Graham (1969)
`earliest-deadline`	最早 deadline 優先（deadline-感知）	Jackson (1955)
`earliest-finish-time`	最早到達可能完了優先	Topcuoglu et al. (2002)
`minimum-slack`	最小スケジュール余裕優先	Conway, Maxwell & Miller (1967)
`greedy-completion`	最早の状態-感知完了優先	Graham (1966)
`apparent-tardiness-cost`	最高の見かけ遅延コスト指数優先（deadline-感知）	Vepsalainen & Morton (1987)

退役したディスパッチングファミリ

資源-写像ファミリは汎用種に静かに畳み込まれる代わりに明示的に記録されます。それらは資源割当を制御し、タスク順序ではなく、需要-尊重の逐次構築器は現在のコア問題モデルの下で自由な写像決定を一切露呈しません：

資源写像（olb、met、mct、round-robin、load-balanced）：構築器は各タスクをその宣言された資源に割り当てるため、これらのヒューリスティックは実現可能な自由度を持ちません；実行-時間-感知の写像は目的と制約の作業に属し、この基盤ではありません。
型タグ不在（type-aware、domain-aware）：現在のコア問題モデルが担わないタスク-型と資源-型のタグを要します。

retired_dispatching_families() はファミリ-毎の根拠と引用を持つ完全な台帳を返します。

ランクベースリストスケジューラ

三つの異種性-感知リスト-スケジューラは二-段階の形を共有します：静的優先度付け段階が平均実行時間と通信コストから優先順位 DAG 上で各タスクをランク付けし、プロセッサ-選択段階が逐次構築器の最早-完了規則を通じて各タスクを優先度順に束縛します。資源-毎の実行時間は可鋳タスクの宣言された実行モードから来ますが、剛タスクは文書化された同種特殊化に退化します。

Solver	優先度基準	正準参照
`heft`	上昇ランク	Topcuoglu, Hariri & Wu (2002)
`cpop`	上昇-下降結合ランク、クリティカルパスタスク優先	Topcuoglu, Hariri & Wu (2002)
`peft`	楽観的コスト表ランク	Arabnejad & Barbosa (2014)

準備集合写像規則

三つのバッチ写像規則は、各優先順位-準備タスクをその候補束縛にわたる最早完了で採点し、次にステップごとに一タスクを写像します。公表された規則は独立タスクのバッチを写像します；ここではバッチは優先順位-準備集合であるため、規則は依存作業負荷へ拡張され、独立-タスクインスタンス上では公表された挙動に帰着します。これらのファミリは、可鋳 TaskSpec.modes がその機械-毎の期待-完了行列を表現可能にした時点で、退役台帳を離れて一級登録へ移りました。

Solver	優先度基準	正準参照
`min-min`	最小の最良完了優先	Ibarra & Kim (1977); Braun et al. (2001)
`max-min`	最大の最良完了優先	Ibarra & Kim (1977); Braun et al. (2001)
`sufferage`	第二最良と最良の完了差が最大のものを優先	Maheswaran et al. (1999)

反復貪欲、タブー、および RCPSP ベースライン

三つの公表された単一-解法は優先順位-安全なタスク順序空間を探索し、各々その正準機構を共有された順序-実行継ぎ目に投影します。serial-sgs-justification はプラットフォーム初の資源-制約付きプロジェクトスケジューリング（RCPSP）ソルバーです。

Solver	機構	正準参照
`iterated-greedy-rs`	破壊-再構築ループと固定-温度受理を伴う NEH シード	Ruiz & Stutzle (2007)
`critical-path-tabu`	クリティカルパスブロック移動上の高度タブー探索	Nowicki & Smutnicki (2005)
`serial-sgs-justification`	二重の右-次いで-左詰めを伴う逐次スケジュール-生成-スキーム復号	Valls, Ballestin & Quintanilla (2005)

メタヒューリスティックベースライン

代表的なシード化メタヒューリスティックベースラインは、同じ実行可能性、修復、採点、および局所-探索演算子を、各々その正準参照とともに共有します：遺伝的アルゴリズム（Holland 1975）、シミュレーテッドアニーリング（Kirkpatrick et al. 1983）、アントコロニー（Dorigo, Maniezzo & Colorni 1996）、粒子群（Kennedy & Eberhart 1995）、および差分進化（Storn & Price 1997）。

符号化アダプタソルバー

包括学習粒子群最適化と成功-履歴適応差分進化は、タスクごとに一つの成分を持つ連続な実-値ベクトルを探索します。各コアは連続目的について汎用であるため、その収束挙動は連続ベンチマーク上で直接検証され、ベクトルを優先順位-実行可能なタスク順序に復号する符号化アダプタを通じてスケジューリング問題に束縛されます。

Solver	着想	適応	符号化
`clpso`	包括学習粒子群（Liang et al. 2006）	優先順位順序へ復号された連続群	random-key
`d-clpso`	包括学習粒子群（Liang et al. 2006）	最小-位置-値復号上の離散アダプタ	spv
`lshade`	線形種群削減を伴う成功-履歴適応 DE（Tanabe & Fukunaga 2014）	優先順位順序へ復号された連続差分進化	random-key
`d-lshade`	線形種群削減を伴う成功-履歴適応 DE（Tanabe & Fukunaga 2014）	整数-順位丸め上の離散アダプタ	rounding

CLPSO は単一の大域-最良ではなく包括-学習の範例から各次元を学習するため、群の最良適合度は単峰盆地上で単調に改善します。L-SHADE はその成功履歴から交叉とスケール-因子の記憶を適応させ、種群を最少四個体まで線形に縮小します。両者は収束軌跡を報告し、収束-正当性テストは観測軌跡が公表された参照挙動と矛盾するとき、シード-再現性検査とは別に失敗します。

これらのソルバーは符号化橋を通じて離散領域に適応された連続最適化器です；それらはいかなる先行コードの厳密な再実装でもなく、比較キャンペーンが走る前にいかなる性能主張もなされません。

符号化アダプタ

各具名の連続-から-離散符号化は明示的な修復方針を持つ独立したアダプタであるため、いかなる符号化も汎用復号器に静かに畳み込まれません。各アダプタはその復号された順序を優先順位-実行可能な順序に修復し、修復が発火したかを記録します。

アダプタ	伝達	復号規則	引用状態
`random-key`	恒等	クランプされたキーでソート	正準（Bean 1994）
`spv`	恒等	最小位置値	正準（Tasgetiren et al. 2007）
`rounding`	恒等	整数順位スロット	非該当
`sigmoid`	S-字シグモイド	重み付き確率抽選	正準（Kennedy & Eberhart 1997）
`v-shaped`	V-字大きさ	重み付き確率抽選	正準（Mirjalili & Lewis 2013）
`tanh`	平行移動双曲正接	重み付き確率抽選	正準（Mirjalili & Lewis 2013）

決定論的復号（random-key、spv、rounding）は乱数源を無視します；伝達-関数復号はシード化された源を消費し、固定シード下でリプレイします。rounding アダプタは引用-非該当を記録します。最近-整数順位丸めは単一の正準的先駆起源を持たない汎用離散化だからです。

多様化された競争者集合

多様化された競争者集合は、各々がその全文参照を順序-のみの決定空間に投影した具名ソルバーである少数の近年の強力な同位で、代表的ベースラインを超えて比較を広げます。古典ベースラインのかさ高い名簿ではなく。同位は会場-強度の閾を越えたときのみ公開競技場に留まります：後-2020 の有効性窓の内に、索引付きで査読された会場で公表され、読者が威信ではなく証拠でそれを重み付けできるよう会場層が記録されます。

Solver	機構	会場	引用
`epso`	パス収集を伴う負荷-偏向-初期化群	Electronics (MDPI), 2023 — 索引付き	Anbarkhan & Rakrouki (2023)
`adpso`	成功-適応的降下慣性を伴う群探索	Sensors (MDPI), 2022 — 索引付き	Nabi et al. (2022)
`ccgp`	優先度-規則木の協調共進化	Computers & Operations Research (Elsevier), 2024 — トップ層 OR	Zaki et al. (2024)

各同位はその強み、留意点、および実行可能-ベースライン証拠クラスをメタデータに担うため、推薦器はソルバーがある文脈になぜ適合するかを説明できます。より広い古典ベースラインの集合——整数-キー、偏向-乱数-キー、および分布-推定遺伝的アルゴリズム、ならびに大-近傍、反復貪欲、タブー、可変-近傍、およびミーム探索——は内部比較のため登録されたままですが、代表的ヒューリスティックが既にその機構信号を担うため、公開競技場の外に保たれます。比較キャンペーンが走る前にいかなる性能主張もなされません。

有効性アンカー

競争者と関連-作業の集合は、後-2020 の有効性アンカーを通じて現-十年の作業に対して位置づけられます：Karimi-Mamaghan、Mohammadi、Pasdeloup、Meyer（2023、順列フローショップのため反復貪欲に統合された Q-learning を介して演算子を選択することを学習、European Journal of Operational Research 304(3):1296-1330, doi:10.1016/j.ejor.2022.03.054）、および分散フローショップ群スケジューリングのための 2024 IEEE Transactions on Evolutionary Computation の指標-駆動協調多-目的進化アルゴリズム（doi:10.1109/TEVC.2023.3339558）。

多目的競争者

二つのパレート競争者は、多数目的 nsga3 表面とは別個に、優先順位-安全なタスク順序の種群を多-目的ベクトルの上で進化させます。nsga2 は優越-基盤の競争者——高速非優越ソートと混雑距離同点解消——であり、moead は分解-基盤の対抗馬で、問題を構造化された単体重みグリッドに沿ったスカラー Tchebycheff 部分問題に分割し、各部分問題の最近-重み近傍にわたって現行解を置換します。

Solver	機構	正準参照
`nsga2`	優越-基盤のパレート：高速非優越ソート、混雑距離	Deb et al. (2002)
`moead`	分解-基盤のパレート：単体重みグリッド上の Tchebycheff スカラー化	Zhang & Li (2007)

多-目的競争者

nsga3 は具名の多-目的競争者（Deb & Jain 2014, doi:10.1109/TEVC.2013.2281535）であり、二-目的競争者表面とは別個です。優先順位-安全なタスク順序の上に種群を維持し、各順序を多-目的ベクトル（makespan、遅延、および負荷公平性）で評価し、単位単体上の Das & Dennis 構造化参照点に対する参照点-ニッチング選択によって各世代を生き延びます。参照点設計、解のその最近参照方向への関連付け、およびニッチ-数選択がアルゴリズム署名です；ソルバーはキャンペーンが明示的にそれを選択できるよう multi-objective と並んで many-objective 能力を宣言します。

能力照合

SolverRegistry.select は能力、制約、および目的でソルバーを照合します。能力と制約の要件は両方能力タグとして表現され、連言として照合されます：deadline-感知も要する多-目的キャンペーンは required_capabilities=(SolverCapability.MANY_OBJECTIVE,) と required_constraints=(SolverCapability.DEADLINE_AWARE,) を渡し、レジストリは両方を宣言するソルバーのみを返します。objective フィルタは結果をその具名目的への対応を宣言するソルバーにさらに制限します。

NDSO ファミリ

NDSO ファミリはレジストリの原生-符号化ソルバーファミリです：実行可能なスケジュールの上を直接探索します。それが生成する各スケジュールは構築によって実行可能であり（妥当性-設計による構築器が各ステップで優先順位-尊重の順序を構築）、そのためファミリは native 符号化を担い、符号化/復号ステップや修復パスを決して走らせません。ファミリは小さな具名機構の集合を構成します：

信頼行列 — より良いスケジュールがそのセルを強化するにつれ更新される、（位置、タスク）セルごとの学習された信頼の疎な格納。
信頼-重み付き投票 — 信頼行列で重み付けされた種群を横断して投票することでエリートスケジュールを合成します；高速変体は代わりに非-重み付き多数投票を用います。
量-と-質スケジュール — 量スケジュールは候補がどれだけ変化するかを設定します；質スケジュールはそれがどの指導源から学習するかを設定します。
三-源指導 — 候補は合成エリート（活用）、同位（多様性）、または消失-知識源（急進的探索）から学習します。
統一適応係数 — 一つの非-線形スケジュールがファミリを探索から活用へ移し、感度と学習焦点の両方を駆動します；高速変体はそれを固定値に固定します。

変体	構成
`ndso-core`	信頼-重み付き投票、適応係数、三-源指導
`ndso-fast`	多数-投票合成、固定係数、単一指導源
`ndso-summit`	総括的合成を伴い複数の群を協調させる群間-評議会

群間-評議会

ndso-summit 変体は質の構成です：複数の群を並列に走らせ、一つの評議会を通じてそれらを協調させます。各群はそれ自身の種群の上にコア機構を構成します；群間-評議会はそれらの群を複数の孤立した実行ではなく一つの探索として働かせ続け、その結果を単一の総括的エリート——峰会全体が支持するスケジュール——に合成します。評議会は一目で変体を区別するものです：ndso-core と ndso-fast は各々一つの種群で探索し、ndso-summit は協調された多-群探索のために構築された構成です。

評議会の協調挙動と群-毎の診断は構成可能であり、それが生成する各スケジュールは構築によって実行可能であり続けます。

消去マップ

消去マップは、下流分析が各機構の貢献を帰属できるよう、具名機構ごとに一つの隔離構成を列挙します。群内エントリは各々一つのコアスイッチ——信頼行列、信頼-重み付き投票、量スケジュール、質スケジュール、多-源指導構造、同位源、消失-知識源、および適応係数——を無効化します。協調エントリは各々一つの評議会パラメータ——群内- 対群間-協調、および総括的合成——を無効化します。各エントリは分析層がその比較をサンプリングする実行-数下限を宣言し、その層が適用する統計検定（一つの対応のある非-パラメトリック有意性検定、Holm 多重-比較補正を伴う Friedman 平均-順位事後検定、および一つの Cliff-delta 効果量）を命名します。サンプリングされた比較は下流の実験エンジンによって物質化されます；プロセス内 runner は各隔離が実行可能であることを証明します。

段階的エクスポートフィルタは、報告範囲がどの機構を露呈できるかを統治します：基盤範囲は基盤的機構のみを露呈し、fail-closed ゲートは基盤範囲下でより-速いまたはより高-質な機構を漏らす代わりに上げるため、基盤範囲と高速範囲は評議会-のみ機構を露呈できません。収束診断——種群多様性、信頼-行列エントロピー、探索比、資源負荷、計時、および改善軌跡——はオプションであり無効化時にオーバーヘッドを加えず、評議会はいかなるソルバー型もインポートせず分析層が取り込む JSON マニフェストをエクスポートします。

厳密アダプタ

厳密アダプタは二つの形態の一つを取ります。オプション-バックエンドアダプタはオプション依存を宣言し実行時にそのインポート根を検査します；バックエンドが不在ならば、パッケージインポート中に重量級依存をインポートする代わりに、extra 名、目的、商用フラグ、および licensing 注記を伴う MissingOptionalDependencyError を上げます。原生-有界アダプタは第三者依存を担わず、自らの有界探索——一方は優先順位-実行可能な順序の列挙、他方は決定図分枝限定——により小さなインスタンスを厳密に解き、インスタンスが対応タスク数を超えるとき UnsupportedCapabilityError を上げます。

Solver	手法	バックエンド	Extra	商用
`ortools-cp-sat`	CP-SAT	`ortools`	`exact`	いいえ
`pulp-milp`	MILP / MIP	`pulp`	`exact`	いいえ
`branch-and-bound`	branch-and-bound / cut	`ortools`	`exact`	いいえ
`logic-based-benders-decomposition`	論理ベース Benders 分解	`pulp`	`exact`	いいえ
`exhaustive-enumeration`	網羅的列挙	原生（バックエンドなし）	—	いいえ
`decision-diagram-sequencing`	決定図分枝限定	原生（バックエンドなし）	—	いいえ
`gurobi-exact`	MILP / MIP	`gurobipy`	`exact-commercial`	はい

公開の厳密な一握りは代表的な開放集合——CP-SAT、MILP、branch-and-bound、および論理ベース Benders 分解——に加え、二つの原生有界ソルバーと真に呼び出す Gurobi ラッパーです。商用 Gurobi ラッパーは exact-commercial extra の背後に留まり、決してバンドルされず、明示的な licensing 注記を担います；学術ライセンスが利用可能です。より広いオプションバックエンドのプールは内部使用のため登録されたままです。

選択と学習層

選択層は、大域-最良ソルバーを決して主張することなく、ある問題に対しソルバーをランク付けします。それは SelectionFeatures 記録——difficulty、heterogeneity、objective_conflict、 uncertainty、dynamism、および solver_sensitivity——と公開ソルバーメタデータを消費し、ランク付けされた SolverRecommendation 行を返します。各推薦は RecommendationSource （metadata または learned-model）、ConfidenceLabel、および明示的制限注記を担うため、メタデータ推薦が学習された推薦と取り違えられることは決してありません。

FEATURE_ORIGIN は各特徴をそれが読むベンチマーク特徴化指標まで追跡します；消費者は dispatchatlas.bench の特徴化指標を特徴契約に写像するため、dispatchatlas.solve は依然 dispatchatlas.core のみをインポートします。二つの選択器が出荷されます：RuleBasedSelector は宣言された能力と特徴化のみからランク付けし（メタデータ推薦）、SupervisedSelector はラベル付き corpus から決定論的な距離-重み付き最近-傍モデルによってランク付けします（学習-モデル推薦）。

有監督選択器は訓練適合ではなく、留め置き汎化を報告します。交叉-検証プロトコルはラベル付き corpus を、いかなるインスタンス、いかなるベンチマークファミリ、いかなる特徴化記録も訓練とテストの両分割に現れないよう分割し（partition_by_families、leave_one_family_out、 leakage_report）、残りのファミリで訓練し、留め置きファミリを採点します（held_out_generalization、cross_validate）。その信頼は、漏洩-のない留め置き評価がそれを支持するときのみ、メタデータ-のみを超えて上昇します。

learning_interface_catalog() は七つの具名の学習・ハイブリッドインターフェース——有監督アルゴリズム選択、代理-支援探索、強化-学習フック、ハイパー-ヒューリスティック、方策-誘導修復、学習された初期化、およびベンチマーク-のみベースライン——を登録します。各々が LearningEvidencePolicy（訓練データ、漏洩制御、再現性、証拠クラス、証拠-層適格性）を宣言します。二つはここで実装されます；他の五つは、その実現が訓練データを生む比較キャンペーンに依存する登録された遅延インターフェースです。重い推定器バックエンドはオプションの learning extra の背後に留まりインポート根で探られ、決してパッケージインポート中にインポートされません；不在は MissingOptionalDependencyError を上げ、決定論的フォールバックは利用可能であり続けます。

性能保護

バッチ採点は BatchScoringProfile を通じて明示的です。現在のカーネルは標準-ライブラリフォールバックを用い、そのフォールバックを診断に記録します。これは API をベクトル化または加速されたカーネルに対し準備されたまま保ちつつ、テスト済みで移植可能な経路を保ちます。