Sistem Solver

dispatchatlas.solve memiliki metadata solver, seleksi, konstruksi jadwal, perbaikan, pengaman kinerja, famili lini-dasar, adaptor opsional, dan famili NDSO.

Paket ini mengimpor hanya dispatchatlas.core. Tes integrasi smoke benchmark hidup di tests/solve/ sehingga paket runtime tidak bergantung pada generator benchmark konkret.

Contoh yang dapat dijalankan: examples/compare_solvers.py menjadwalkan sebuah kohort solver secara paralel dan menambatkannya dengan sebuah optimum eksak yang terbukti.

Registri

SolverRegistry menyimpan pabrik solver tanpa-keadaan dengan metadata kaya:

label kapabilitas seperti capacity-aware, precedence-aware, repair, local-search, dan ndso
tujuan yang didukung seperti makespan, energy, dan cost
batasan yang dideklarasikan diekspresikan melalui label kapabilitas
kriteria henti default
perilaku replay deterministik atau stokastik-tersemai
pengkodean solusi (permutation, mapping, assignment, atau native)
deklarasi dependensi opsional dan pengungkapan backend-komersial
visibilitas tingkat-bukti untuk ekspor bertahap
sebuah sitasi kanonik, atau sebuah alasan eksplisit sitasi-tidak-berlaku

Setiap famili solver bernama membawa sebuah sitasi yang gagal tertutup: sebuah famili dengan asal seminal kanonik yang menghilangkan referensinya tidak dapat dikonstruksi, dan sebuah famili tanpa asal kanonik tunggal mencatat alasannya alih-alih memfabrikasi satu.

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

Katalog Registri

Setiap solver di registri, dalam satu tabel yang dapat-diurutkan dan dapat-dicari. Tabel dan totalnya dihasilkan dari default_solver_registry(), sehingga hitungan di bawah dapat dihitung dari baris itu sendiri. Sebuah diagram cakupan-kapabilitas di atas tabel merangkum berapa banyak solver registri yang mendeklarasikan setiap kapabilitas yang dideklarasikan.

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.

Keberlakuan Solver

Solver mana yang berlaku untuk keluarga tolok ukur mana, dibaca dari matriks keberlakuan. Setiap sel adalah keberlakuan yang dinyatakan, bukan klaim kinerja: sel terverifikasi mengutip kampanye publik, sitasi, atau pengujian bernama, dan sel perkiraan diturunkan dari kapabilitas solver yang dinyatakan dan sifat keluarga tolok ukur. Tabel di bawah dihasilkan dari bundel keberlakuan publik, sehingga total statusnya dapat dihitung dari barisnya sendiri.

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.

Lini-Dasar Pendispatchan

Lini-dasar deterministik yang dibundel adalah solver penjadwalan-daftar konstruktif. Masing-masing mengontrol urutan tugas; konstruktor serial menghormati permintaan sumber daya yang dideklarasikan setiap tugas dan menugaskan setiap tugas ke sumber daya yang diminta yang paling-cepat-tersedia. Setiap famili menamai referensi seminal kanoniknya.

Solver	Basis prioritas	Referensi kanonik
`earliest-start`	urutan masukan topologis	tidak berlaku (lini-dasar identitas)
`shortest-processing-time`	tugas durasi terpendek dahulu	Smith (1956)
`longest-processing-time`	tugas durasi terpanjang dahulu	Graham (1969)
`earliest-deadline`	deadline paling cepat dahulu (sadar-deadline)	Jackson (1955)
`earliest-finish-time`	penyelesaian terjangkau paling cepat dahulu	Topcuoglu et al. (2002)
`minimum-slack`	slack jadwal terkecil dahulu	Conway, Maxwell & Miller (1967)
`greedy-completion`	penyelesaian sadar-keadaan paling cepat dahulu	Graham (1966)
`apparent-tardiness-cost`	indeks biaya-keterlambatan-tampak tertinggi dahulu (sadar-deadline)	Vepsalainen & Morton (1987)

Famili Pendispatchan yang Dipensiunkan

Famili pemetaan-sumber-daya dicatat secara eksplisit alih-alih dilipat diam-diam ke dalam jenis generik. Mereka mengontrol penugasan sumber daya, bukan urutan tugas, dan konstruktor serial penghormat-permintaan tidak mengekspos keputusan pemetaan bebas apa pun di bawah model masalah inti saat ini:

Pemetaan sumber daya (olb, met, mct, round-robin, load-balanced): konstruktor menugaskan setiap tugas ke sumber daya yang dideklarasikannya, sehingga heuristik ini tidak memiliki derajat kebebasan yang dapat-direalisasikan; pemetaan sadar-waktu-eksekusi dimiliki oleh pekerjaan tujuan dan batasan, bukan fondasi ini.
Label jenis absen (type-aware, domain-aware): memerlukan label jenis-tugas dan jenis-sumber-daya yang tidak dibawa model masalah inti saat ini.

retired_dispatching_families() mengembalikan buku-besar lengkap dengan alasan dan sitasi per-famili.

Penjadwal-Daftar Berbasis-Peringkat

Tiga penjadwal-daftar sadar-heterogenitas berbagi bentuk dua-fase: sebuah fase prioritisasi statis merangking setiap tugas atas DAG presedensi dari waktu eksekusi rata-rata dan biaya komunikasi, dan sebuah fase seleksi-prosesor menambatkan setiap tugas dalam urutan prioritas melalui aturan penyelesaian-paling-cepat konstruktor serial. Waktu eksekusi per-sumber-daya berasal dari mode eksekusi yang dideklarasikan sebuah tugas dapat-dibentuk, dan sebuah tugas kaku merosot ke spesialisasi homogen yang terdokumentasi.

Solver	Basis prioritas	Referensi kanonik
`heft`	peringkat naik	Topcuoglu, Hariri & Wu (2002)
`cpop`	peringkat naik-plus-turun gabungan, tugas jalur-kritis dahulu	Topcuoglu, Hariri & Wu (2002)
`peft`	peringkat tabel-biaya-optimistik	Arabnejad & Barbosa (2014)

Aturan Pemetaan Himpunan-Siap

Tiga aturan pemetaan batch menskor setiap tugas siap-presedensi menurut penyelesaian paling-cepatnya atas tambatan kandidatnya, lalu memetakan satu tugas per langkah. Aturan yang diterbitkan memetakan sebuah batch tugas independen; di sini batch adalah himpunan siap-presedensi, sehingga aturan meluas ke beban kerja dependen dan menyusut ke perilaku yang diterbitkan pada instans tugas-independen. Famili-famili ini meninggalkan buku-besar pensiun untuk registrasi kelas-utama begitu TaskSpec.modes yang dapat-dibentuk membuat matriks penyelesaian-diharapkan per-mesin mereka dapat-direpresentasikan.

Solver	Basis prioritas	Referensi kanonik
`min-min`	penyelesaian terbaik terkecil berikutnya	Ibarra & Kim (1977); Braun et al. (2001)
`max-min`	penyelesaian terbaik terbesar berikutnya	Ibarra & Kim (1977); Braun et al. (2001)
`sufferage`	selisih penyelesaian terbaik-kedua-dikurangi-terbaik terbesar berikutnya	Maheswaran et al. (1999)

Lini-Dasar Serakah-Teriterasi, Tabu, dan RCPSP

Tiga metode solusi-tunggal yang diterbitkan mencari ruang urutan-tugas aman-presedensi, masing-masing memproyeksikan mekanisme kanoniknya ke jahitan urutan-jalan bersama. serial-sgs-justification adalah solver penjadwalan-proyek berbatasan-sumber-daya (RCPSP) pertama pada platform ini.

Solver	Mekanisme	Referensi kanonik
`iterated-greedy-rs`	benih NEH dengan loop penghancuran-rekonstruksi dan penerimaan suhu-tetap	Ruiz & Stutzle (2007)
`critical-path-tabu`	pencarian tabu lanjutan atas gerakan blok jalur-kritis	Nowicki & Smutnicki (2005)
`serial-sgs-justification`	dekode skema-penghasil-jadwal serial dengan justifikasi ganda kanan-lalu-kiri	Valls, Ballestin & Quintanilla (2005)

Lini-Dasar Metaheuristik

Lini-dasar metaheuristik tersemai representatif berbagi operator kelayakan, perbaikan, penskoran, dan pencarian-lokal yang sama, masing-masing dengan referensi kanoniknya: algoritma genetik (Holland 1975), simulated annealing (Kirkpatrick et al. 1983), koloni semut (Dorigo, Maniezzo & Colorni 1996), kawanan partikel (Kennedy & Eberhart 1995), dan evolusi diferensial (Storn & Price 1997).

Solver Adaptor Terkode

Optimisasi kawanan partikel pembelajaran komprehensif dan evolusi diferensial adaptif riwayat-keberhasilan mencari sebuah vektor kontinu bernilai-real dengan satu komponen per tugas. Setiap inti generik atas sebuah tujuan kontinu, sehingga perilaku konvergensinya divalidasi langsung pada sebuah benchmark kontinu, dan diikat ke masalah penjadwalan melalui sebuah adaptor pengkodean yang mendekode sebuah vektor menjadi sebuah urutan tugas layak-presedensi.

Solver	Inspirasi	Adaptasi	Pengkodean
`clpso`	Kawanan partikel pembelajaran komprehensif (Liang et al. 2006)	Kawanan kontinu didekode ke urutan presedensi	random-key
`d-clpso`	Kawanan partikel pembelajaran komprehensif (Liang et al. 2006)	Adaptor diskret atas dekode nilai-posisi-terkecil	spv
`lshade`	DE adaptif riwayat-keberhasilan dengan reduksi populasi linier (Tanabe & Fukunaga 2014)	Evolusi diferensial kontinu didekode ke urutan presedensi	random-key
`d-lshade`	DE adaptif riwayat-keberhasilan dengan reduksi populasi linier (Tanabe & Fukunaga 2014)	Adaptor diskret atas pembulatan peringkat-bulat	rounding

CLPSO mempelajari setiap dimensi dari sebuah eksemplar pembelajaran-komprehensif alih-alih sebuah terbaik-global tunggal, sehingga kebugaran terbaik kawanan membaik secara monoton pada sebuah cekungan unimodal. L-SHADE mengadaptasi memori crossover dan faktor-skala dari riwayat keberhasilannya dan menyusutkan populasi secara linier hingga minimum empat individu. Keduanya melaporkan sebuah lintasan konvergensi, dan sebuah tes kebenaran-konvergensi gagal ketika lintasan teramati bertentangan dengan perilaku referensi yang diterbitkan, terpisah dari pemeriksaan reproduktibilitas-benih.

Solver-solver ini adalah pengoptimal kontinu yang diadaptasi ke domain diskret melalui sebuah jembatan pengkodean; mereka bukan reimplementasi eksak dari kode sebelumnya mana pun, dan tidak ada klaim kinerja yang dibuat sebelum kampanye komparatif berjalan.

Adaptor Pengkodean

Setiap pengkodean kontinu-ke-diskret bernama adalah adaptor berbeda dengan kebijakan perbaikan eksplisit, sehingga tidak ada pengkodean yang dilipat diam-diam ke dalam sebuah dekoder generik. Setiap adaptor memperbaiki urutan terdekodenya menjadi sebuah urutan layak-presedensi dan mencatat apakah perbaikan terpicu.

Adaptor	Transfer	Aturan dekode	Status sitasi
`random-key`	identitas	urutkan menurut kunci terjepit	kanonik (Bean 1994)
`spv`	identitas	nilai posisi terkecil	kanonik (Tasgetiren et al. 2007)
`rounding`	identitas	slot peringkat bulat	tidak-berlaku
`sigmoid`	sigmoid S	undian probabilistik berbobot	kanonik (Kennedy & Eberhart 1997)
`v-shaped`	magnitudo V	undian probabilistik berbobot	kanonik (Mirjalili & Lewis 2013)
`tanh`	tangen hiperbolik tergeser	undian probabilistik berbobot	kanonik (Mirjalili & Lewis 2013)

Dekode deterministik (random-key, spv, rounding) mengabaikan sumber acak; dekode fungsi-transfer mengonsumsi sebuah sumber tersemai dan mengulang di bawah sebuah benih tetap. Adaptor rounding mencatat sitasi-tidak-berlaku karena pembulatan peringkat ke-bilangan-bulat-terdekat adalah sebuah diskretisasi generik tanpa asal seminal kanonik tunggal.

Himpunan Pesaing Terdiversifikasi

Himpunan pesaing terdiversifikasi memperluas perbandingan melampaui lini-dasar representatif dengan segenggam kecil rekan kuat terkini, masing-masing sebuah solver bernama dengan referensi teks-lengkapnya yang diproyeksikan ke ruang keputusan hanya-urutan, alih-alih sebuah daftar besar lini-dasar klasik. Sebuah rekan tetap di lapangan publik hanya ketika ia melewati sebuah bilah kekuatan-tempat: diterbitkan dalam jendela kekinian pasca-2020 di sebuah tempat terindeks, ditinjau-sejawat, dengan tingkat tempat dicatat agar pembaca menimbangnya berdasarkan bukti alih-alih prestise.

Solver	Mekanisme	Tempat	Sitasi
`epso`	Kawanan inisialisasi-terbias-beban-kerja dengan pengumpulan jalur	Electronics (MDPI), 2023 — terindeks	Anbarkhan & Rakrouki (2023)
`adpso`	Pencarian kawanan dengan inersia menurun adaptif-keberhasilan	Sensors (MDPI), 2022 — terindeks	Nabi et al. (2022)
`ccgp`	Koevolusi kooperatif pohon aturan-prioritas	Computers & Operations Research (Elsevier), 2024 — OR tingkat-atas	Zaki et al. (2024)

Setiap rekan membawa kekuatan, peringatan, dan sebuah kelas bukti lini-dasar-dapat-dijalankan dalam metadatanya agar perekomendasi dapat menjelaskan mengapa sebuah solver cocok untuk sebuah konteks. Sebuah himpunan lini-dasar klasik yang lebih luas — algoritma genetik kunci-bulat, kunci-acak-terbias, dan estimasi-distribusi, serta pencarian lingkungan-besar, serakah-teriterasi, tabu, lingkungan-variabel, dan memetik — tetap terdaftar untuk perbandingan internal tetapi dijaga di luar lapangan publik, karena heuristik representatif sudah membawa sinyal mekanismenya. Tidak ada klaim kinerja yang dibuat sebelum kampanye komparatif berjalan.

Jangkar Kekinian

Himpunan pesaing dan pekerjaan-terkait diposisikan terhadap pekerjaan dekade-saat-ini melalui sebuah jangkar kekinian pasca-2020: Karimi-Mamaghan, Mohammadi, Pasdeloup, dan Meyer (2023, belajar memilih operator via Q-learning yang diintegrasikan ke serakah teriterasi untuk flowshop permutasi, European Journal of Operational Research 304(3):1296-1330, doi:10.1016/j.ejor.2022.03.054) dan algoritma evolusioner multi-tujuan kolaboratif terdorong-indikator 2024 IEEE Transactions on Evolutionary Computation untuk penjadwalan grup flowshop terdistribusi (doi:10.1109/TEVC.2023.3339558).

Pesaing Multi-Tujuan

Dua pesaing Pareto mengevolusikan sebuah populasi urutan tugas aman-presedensi pada sebuah vektor multi-tujuan, berbeda dari permukaan nsga3 banyak-tujuan. nsga2 adalah pesaing berbasis-dominasi — pengurutan tak-terdominasi cepat dengan pemecahan-seri jarak-kerumunan — dan moead adalah penyeimbang berbasis-dekomposisi, memecah masalah menjadi submasalah Tchebycheff skalar sepanjang sebuah kisi bobot simpleks terstruktur dan menggantikan petahana lintas lingkungan bobot-terdekat setiap submasalah.

Solver	Mekanisme	Referensi kanonik
`nsga2`	Pareto berbasis-dominasi: pengurutan tak-terdominasi cepat, jarak kerumunan	Deb et al. (2002)
`moead`	Pareto berbasis-dekomposisi: skalarisasi Tchebycheff atas sebuah kisi bobot simpleks	Zhang & Li (2007)

Pesaing Banyak-Tujuan

nsga3 adalah pesaing banyak-tujuan bernama (Deb & Jain 2014, doi:10.1109/TEVC.2013.2281535), berbeda dari permukaan pesaing dwi-tujuan. Ia memelihara sebuah populasi atas urutan tugas aman-presedensi, mengevaluasi setiap urutan pada sebuah vektor multi-tujuan (makespan, keterlambatan, dan keadilan beban), dan bertahan setiap generasi melalui sebuah seleksi pencerukan-titik-referensi atas titik referensi terstruktur Das & Dennis pada simpleks satuan. Desain titik-referensi, asosiasi solusi ke arah referensi terdekatnya, dan seleksi hitungan-ceruk adalah tanda-tangan algoritmik; solver mendeklarasikan kapabilitas many-objective bersama multi-objective agar sebuah kampanye dapat memilihnya secara eksplisit.

Pencocokan Kapabilitas

SolverRegistry.select mencocokkan solver menurut kapabilitas, batasan, dan tujuan. Persyaratan kapabilitas dan batasan keduanya diekspresikan sebagai label kapabilitas dan dicocokkan sebagai sebuah konjungsi: sebuah kampanye banyak-tujuan yang juga memerlukan kesadaran-deadline meneruskan required_capabilities=(SolverCapability.MANY_OBJECTIVE,) dan required_constraints=(SolverCapability.DEADLINE_AWARE,), dan registri mengembalikan hanya solver yang mendeklarasikan keduanya. Sebuah filter objective lebih lanjut membatasi hasil ke solver yang mendeklarasikan dukungan untuk tujuan bernama itu.

Famili NDSO

Famili NDSO adalah famili solver pengkodean-asli registri: ia mencari langsung atas jadwal yang layak. Setiap jadwal yang diproduksinya layak menurut konstruksi (sebuah konstruktor validitas-menurut-desain membangun sebuah urutan penghormat-presedensi di setiap langkah), sehingga famili membawa pengkodean native dan tidak pernah menjalankan langkah enkode/dekode atau lintasan perbaikan. Famili menyusun sebuah himpunan kecil mekanisme bernama:

Matriks Keyakinan — sebuah penyimpanan jarang kepercayaan terpelajar per sel (posisi, tugas), diperbarui seiring jadwal yang lebih baik memperkuat selnya.
Pemungutan-Suara Terbobot-Keyakinan — mensintesis jadwal elit dengan memungut suara lintas populasi yang dibobot oleh Matriks Keyakinan; varian cepat menggunakan sebuah pemungutan suara mayoritas tak-terbobot sebagai gantinya.
Jadwal kuantitas-dan-kualitas — jadwal Kuantitas menetapkan seberapa banyak sebuah kandidat berubah; jadwal Kualitas menetapkan dari sumber panduan mana ia belajar.
Panduan tiga-sumber — sebuah kandidat belajar dari elit tersintesis (eksploitasi), sebuah rekan (keragaman), atau sebuah sumber pengetahuan-lenyap (eksplorasi radikal).
Koefisien adaptif terpadu — satu jadwal non-linier menggeser famili dari eksplorasi ke eksploitasi dan menggerakkan baik sensitivitas maupun fokus-pembelajaran; varian cepat menyematkannya ke sebuah nilai tetap.

Varian	Komposisi
`ndso-core`	Pemungutan-Suara Terbobot-Keyakinan, koefisien adaptif, panduan tiga-sumber
`ndso-fast`	sintesis suara-mayoritas, koefisien tetap, sumber panduan tunggal
`ndso-summit`	Dewan Antar-Kawanan yang mengoordinasi beberapa kawanan dengan sintesis menyeluruh

Dewan Antar-Kawanan

Varian ndso-summit adalah komposisi kualitas: ia menjalankan beberapa kawanan secara paralel dan mengoordinasinya melalui satu dewan. Setiap kawanan menyusun mekanisme inti atas populasinya sendiri; dewan Antar-Kawanan menjaga kawanan-kawanan itu bekerja sebagai satu pencarian alih-alih beberapa jalannya terisolasi dan mensintesis hasilnya menjadi sebuah elit menyeluruh tunggal — jadwal yang di belakangnya seluruh puncak berdiri. Dewan adalah yang membedakan varian sekilas: ndso-core dan ndso-fast masing-masing mencari dengan satu populasi, sementara ndso-summit adalah komposisi yang dibangun untuk pencarian multi-kawanan terkoordinasi.

Perilaku koordinasi dewan dan diagnostik per-kawanan dapat-dikonfigurasi, dan setiap jadwal yang diproduksinya tetap layak menurut konstruksi.

Peta Ablasi

Peta ablasi menumerasi satu konfigurasi pengisolasi per mekanisme bernama agar analisis hilir dapat mengatribusikan kontribusi setiap mekanisme. Entri intra-kawanan masing-masing menonaktifkan satu sakelar inti — Matriks Keyakinan, Pemungutan-Suara Terbobot-Keyakinan, jadwal Kuantitas, jadwal Kualitas, struktur panduan multi-sumber, sumber rekan, sumber pengetahuan-lenyap, dan koefisien adaptif. Entri koordinasi masing-masing menonaktifkan satu parameter dewan — koordinasi Intra- versus Antar-Kawanan, dan sintesis menyeluruh. Setiap entri mendeklarasikan lantai hitungan-jalan di mana lapisan analisis menyampel perbandingannya dan menamai uji statistik yang diterapkan lapisan itu (sebuah uji signifikansi non-parametrik berpasangan, sebuah pasca-hoc peringkat-rata-rata Friedman dengan sebuah koreksi perbandingan-ganda Holm, dan sebuah ukuran-efek Cliff-delta). Perbandingan tersampel dimaterialisasi oleh mesin eksperimen hilir; runner dalam-proses membuktikan setiap isolasi layak.

Sebuah filter ekspor bertahap mengatur mekanisme mana yang dapat diekspos sebuah cakupan laporan: cakupan dasar mengekspos hanya mekanisme fondasional, dan sebuah gerbang fail-closed mengangkat alih-alih membocorkan sebuah mekanisme yang lebih-cepat atau lebih-tinggi-kualitas di bawah sebuah cakupan dasar, sehingga cakupan fondasional dan cepat tidak dapat mengekspos mekanisme hanya-dewan. Diagnostik konvergensi — keragaman populasi, entropi Matriks-Keyakinan, rasio eksplorasi, beban sumber daya, pewaktuan, dan jejak perbaikan — bersifat opsional dan tidak menambah overhead ketika dinonaktifkan, dan dewan mengekspor sebuah manifes JSON yang dicerna lapisan analisis tanpa mengimpor jenis solver apa pun.

Adaptor Eksak

Adaptor eksak mengambil salah satu dari dua bentuk. Adaptor backend-opsional mendeklarasikan sebuah dependensi opsional dan memeriksa akar impornya saat runtime; jika backend absen mereka mengangkat MissingOptionalDependencyError dengan nama extra, tujuan, bendera komersial, dan catatan lisensi alih-alih mengimpor sebuah dependensi berat selama impor paket. Adaptor asli-terbatas tidak membawa dependensi pihak-ketiga dan menyelesaikan instans kecil secara eksak dengan pencarian terbatasnya sendiri — menumerasi urutan layak-presedensi dalam satu kasus, branch-and-bound diagram-keputusan dalam yang lain — mengangkat UnsupportedCapabilityError ketika instans melebihi hitungan tugas yang didukung.

Solver	Metode	Backend	Extra	Komersial
`ortools-cp-sat`	CP-SAT	`ortools`	`exact`	tidak
`pulp-milp`	MILP / MIP	`pulp`	`exact`	tidak
`branch-and-bound`	branch-and-bound / cut	`ortools`	`exact`	tidak
`logic-based-benders-decomposition`	dekomposisi Benders berbasis-logika	`pulp`	`exact`	tidak
`exhaustive-enumeration`	enumerasi ekshaustif	asli (tanpa backend)	—	tidak
`decision-diagram-sequencing`	branch-and-bound diagram-keputusan	asli (tanpa backend)	—	tidak
`gurobi-exact`	MILP / MIP	`gurobipy`	`exact-commercial`	ya

Segenggam eksak publik adalah himpunan terbuka representatif — CP-SAT, MILP, branch-and-bound, dan dekomposisi Benders berbasis-logika — ditambah dua solver asli terbatas dan pembungkus Gurobi yang sungguh-memanggil. Pembungkus Gurobi komersial tetap di belakang extra exact-commercial, tidak pernah dibundel, dan membawa sebuah catatan lisensi eksplisit; sebuah lisensi akademik tersedia. Sebuah kumpulan backend opsional yang lebih luas tetap terdaftar untuk penggunaan internal.

Lapisan Seleksi dan Pembelajaran

Lapisan seleksi merangking solver untuk sebuah masalah tanpa pernah mengklaim sebuah solver terbaik secara global. Ia mengonsumsi sebuah rekaman SelectionFeatures — difficulty, heterogeneity, objective_conflict, uncertainty, dynamism, dan solver_sensitivity — dan metadata solver publik, dan mengembalikan baris SolverRecommendation terangking. Setiap rekomendasi membawa sebuah RecommendationSource (metadata atau learned-model), sebuah ConfidenceLabel, dan catatan keterbatasan eksplisit, sehingga sebuah rekomendasi metadata tidak pernah disalahartikan sebagai yang terpelajar.

FEATURE_ORIGIN melacak setiap fitur ke metrik karakterisasi benchmark yang dibacanya; konsumer memetakan metrik karakterisasi dispatchatlas.bench ke dalam kontrak fitur, sehingga dispatchatlas.solve masih mengimpor hanya dispatchatlas.core. Dua selektor dikapalkan: RuleBasedSelector merangking dari kapabilitas yang dideklarasikan dan karakterisasi saja (sebuah rekomendasi metadata), dan SupervisedSelector merangking dari sebuah korpus berlabel oleh sebuah model tetangga-terdekat terbobot-jarak deterministik (sebuah rekomendasi model-terpelajar).

Selektor tersupervisi melaporkan generalisasi tertahan, tidak pernah kecocokan pelatihan. Protokol validasi-silang mempartisi sebuah korpus berlabel sehingga tidak ada instans, tidak ada famili benchmark, dan tidak ada rekaman karakterisasi muncul di kedua partisi pelatihan dan uji (partition_by_families, leave_one_family_out, leakage_report), berlatih pada famili sisanya, dan menskor famili tertahan (held_out_generalization, cross_validate). Keyakinannya naik di atas hanya-metadata hanya ketika sebuah evaluasi tertahan bebas-bocoran mendukungnya.

learning_interface_catalog() mendaftarkan tujuh antarmuka pembelajaran dan hibrida bernama — seleksi algoritma tersupervisi, pencarian terbantu-surogat, kait pembelajaran-penguatan, hiper-heuristik, perbaikan terpandu-kebijakan, inisialisasi terpelajar, dan sebuah lini-dasar hanya-benchmark. Masing-masing mendeklarasikan sebuah LearningEvidencePolicy (data pelatihan, kontrol kebocoran, reproduktibilitas, kelas bukti, kelayakan tingkat-bukti). Dua diimplementasikan di sini; lima lainnya adalah antarmuka tertunda terdaftar yang realisasinya digerbangi pada kampanye komparatif yang menghasilkan data pelatihan. Backend estimator berat tetap di belakang extra learning opsional dan diselidik menurut akar impor, tidak pernah diimpor selama impor paket; ketiadaan mengangkat MissingOptionalDependencyError sementara fallback deterministik tetap tersedia.

Pengaman Kinerja

Penskoran batch eksplisit melalui BatchScoringProfile. Kernel saat ini menggunakan sebuah fallback pustaka-standar dan mencatat fallback itu dalam diagnostik. Ini menjaga API siap untuk kernel tervektorisasi atau terakselerasi sambil memelihara sebuah jalur teruji dan portabel.