सॉल्वर प्रणाली

dispatchatlas.solve सॉल्वर मेटाडेटा, चयन, शेड्यूल निर्माण, मरम्मत, प्रदर्शन रक्षोपाय, आधार-रेखा परिवारों, वैकल्पिक एडाप्टरों, और NDSO परिवार का स्वामी है।

यह पैकेज केवल dispatchatlas.core आयात करता है। बेंचमार्क स्मोक एकीकरण परीक्षण tests/solve/ में रहते हैं ताकि रनटाइम पैकेज ठोस बेंचमार्क जनरेटरों पर निर्भर न हो।

चलाने-योग्य उदाहरण: examples/compare_solvers.py सॉल्वरों की एक समूह को समानांतर में शेड्यूल करता है और इसे एक सिद्ध सटीक इष्टतम से लंगर डालता है।

रजिस्ट्री

SolverRegistry समृद्ध मेटाडेटा के साथ अवस्था-रहित सॉल्वर फ़ैक्टरियाँ संग्रहीत करता है:

क्षमता टैग जैसे capacity-aware, precedence-aware, repair, local-search, और ndso
समर्थित उद्देश्य जैसे makespan, energy, और cost
क्षमता टैगों के माध्यम से व्यक्त घोषित बाधाएँ
डिफ़ॉल्ट विराम मानदंड
नियतात्मक या बीज-युक्त प्रसंभाव्य पुनः-प्रस्तुति व्यवहार
समाधान कूटलेखन (permutation, mapping, assignment, या native)
वैकल्पिक निर्भरता घोषणाएँ और वाणिज्यिक-बैकएंड प्रकटीकरण
चरणबद्ध निर्यातों के लिए साक्ष्य-श्रेणी दृश्यता
एक प्रामाणिक उद्धरण, या एक स्पष्ट उद्धरण-अनुपयुक्त औचित्य

प्रत्येक नामित सॉल्वर परिवार एक उद्धरण धारण करता है जो बंद-होकर विफल होता है: एक प्रामाणिक आद्य मूल वाला परिवार जो अपना संदर्भ छोड़ देता है, निर्मित नहीं किया जा सकता, और एकल प्रामाणिक मूल रहित परिवार एक को गढ़ने के बजाय कारण दर्ज करता है।

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

रजिस्ट्री कैटलॉग

रजिस्ट्री का प्रत्येक सॉल्वर, एक छँटाई-योग्य, खोज-योग्य तालिका में। तालिका और उसके योग default_solver_registry() से उत्पन्न होते हैं, अतः नीचे की गणनाएँ पंक्तियों से ही गणनीय हैं। तालिका के ऊपर एक क्षमता-आवरण रेखाचित्र सारांशित करता है कि कितने रजिस्ट्री सॉल्वर प्रत्येक घोषित क्षमता प्रस्तुत करते हैं।

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.

सॉल्वर प्रयोज्यता

कौन-से सॉल्वर किस बेंचमार्क परिवार पर लागू होते हैं, यह प्रयोज्यता मैट्रिक्स से पढ़ा जाता है। प्रत्येक सेल घोषित प्रयोज्यता है, कभी भी प्रदर्शन का दावा नहीं: सत्यापित सेल किसी नामित सार्वजनिक अभियान, उद्धरण या परीक्षण का हवाला देती हैं, और अनुमानित सेल घोषित सॉल्वर क्षमताओं और बेंचमार्क-परिवार विशेषताओं से व्युत्पन्न होती हैं। नीचे दी गई तालिका सार्वजनिक प्रयोज्यता बंडल से उत्पन्न होती है, इसलिए इसके स्थिति योग पंक्तियों से ही गिने जा सकते हैं।

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.

प्रेषण आधार-रेखाएँ

बंडल की गई नियतात्मक आधार-रेखाएँ रचनात्मक सूची-शेड्यूलिंग सॉल्वर हैं। प्रत्येक कार्य क्रम को नियंत्रित करता है; क्रमिक निर्माता प्रत्येक कार्य की घोषित संसाधन माँगों का सम्मान करता है और प्रत्येक कार्य को उसके सबसे-शीघ्र-उपलब्ध माँगे गए संसाधन को सौंपता है। प्रत्येक परिवार अपना प्रामाणिक आद्य संदर्भ नामित करता है।

Solver	प्राथमिकता आधार	प्रामाणिक संदर्भ
`earliest-start`	सांस्थितिक इनपुट क्रम	अनुपयुक्त (तत्समक आधार-रेखा)
`shortest-processing-time`	सबसे छोटी कार्य अवधि पहले	Smith (1956)
`longest-processing-time`	सबसे लंबी कार्य अवधि पहले	Graham (1969)
`earliest-deadline`	सबसे शीघ्र deadline पहले (deadline-सजग)	Jackson (1955)
`earliest-finish-time`	सबसे शीघ्र पहुँच-योग्य समाप्ति पहले	Topcuoglu et al. (2002)
`minimum-slack`	सबसे छोटी शेड्यूल ढील पहले	Conway, Maxwell & Miller (1967)
`greedy-completion`	सबसे शीघ्र स्थिति-सजग समापन पहले	Graham (1966)
`apparent-tardiness-cost`	उच्चतम प्रकट-विलंबता-लागत सूचकांक पहले (deadline-सजग)	Vepsalainen & Morton (1987)

सेवानिवृत्त प्रेषण परिवार

संसाधन-मानचित्रण परिवारों को एक सामान्य प्रकार में चुपचाप मोड़ने के बजाय स्पष्ट रूप से दर्ज किया जाता है। वे संसाधन आवंटन को नियंत्रित करते हैं, कार्य क्रम को नहीं, और माँग-सम्मान करने वाला क्रमिक निर्माता वर्तमान कोर समस्या मॉडल के अंतर्गत कोई मुक्त मानचित्रण निर्णय उजागर नहीं करता:

संसाधन मानचित्रण (olb, met, mct, round-robin, load-balanced): निर्माता प्रत्येक कार्य को उसके घोषित संसाधन को सौंपता है, अतः इन अनुमानिकाओं के पास कोई साकार-योग्य स्वतंत्रता की कोटि नहीं है; निष्पादन-समय-सजग मानचित्रण उद्देश्यों और बाधाओं के कार्य का स्वामित्व है, इस आधार का नहीं।
प्रकार टैग अनुपस्थित (type-aware, domain-aware): वर्तमान कोर समस्या मॉडल द्वारा न धारण किए गए कार्य-प्रकार और संसाधन-प्रकार टैगों की आवश्यकता रखते हैं।

retired_dispatching_families() प्रति-परिवार औचित्य और उद्धरण के साथ पूर्ण बही लौटाता है।

रैंक-आधारित सूची शेड्यूलर

तीन विषमता-सजग सूची शेड्यूलर एक द्वि-चरणीय आकार साझा करते हैं: एक स्थैतिक प्राथमिकता-निर्धारण चरण औसत निष्पादन समयों और संचार लागतों से पूर्वता DAG पर प्रत्येक कार्य को रैंक करता है, और एक प्रोसेसर-चयन चरण प्रत्येक कार्य को प्राथमिकता क्रम में क्रमिक निर्माता के सबसे-शीघ्र-समाप्ति नियम के माध्यम से बाँधता है। प्रति-संसाधन निष्पादन समय एक ढलाऊ कार्य की घोषित निष्पादन विधाओं से आते हैं, और एक दृढ़ कार्य प्रलेखित सजातीय विशेषीकरण में पतित हो जाता है।

Solver	प्राथमिकता आधार	प्रामाणिक संदर्भ
`heft`	ऊर्ध्वगामी रैंक	Topcuoglu, Hariri & Wu (2002)
`cpop`	संयुक्त ऊर्ध्वगामी-धन-अधोगामी रैंक, क्रांतिक-पथ कार्य पहले	Topcuoglu, Hariri & Wu (2002)
`peft`	आशावादी-लागत-तालिका रैंक	Arabnejad & Barbosa (2014)

तैयार-समुच्चय मानचित्रण नियम

तीन बैच मानचित्रण नियम प्रत्येक पूर्वता-तैयार कार्य को उसके उम्मीदवार बंधनों पर उसके सबसे-शीघ्र समापन द्वारा अंकित करते हैं, फिर प्रति चरण एक कार्य मानचित्रित करते हैं। प्रकाशित नियम स्वतंत्र कार्यों के एक बैच को मानचित्रित करते हैं; यहाँ बैच पूर्वता-तैयार समुच्चय है, अतः नियम आश्रित कार्यभारों तक विस्तृत होते हैं और स्वतंत्र-कार्य इंस्टेंसों पर प्रकाशित व्यवहार में न्यूनित हो जाते हैं। ये परिवार प्रथम-श्रेणी पंजीकरण के लिए सेवानिवृत्त बही छोड़ गए जब ढलाऊ TaskSpec.modes ने उनके प्रति-मशीन अपेक्षित-समापन आव्यूह को निरूपणीय बना दिया।

Solver	प्राथमिकता आधार	प्रामाणिक संदर्भ
`min-min`	सबसे छोटा श्रेष्ठ समापन अगला	Ibarra & Kim (1977); Braun et al. (2001)
`max-min`	सबसे बड़ा श्रेष्ठ समापन अगला	Ibarra & Kim (1977); Braun et al. (2001)
`sufferage`	सबसे बड़ा द्वितीय-श्रेष्ठ-घटा-श्रेष्ठ समापन अंतर अगला	Maheswaran et al. (1999)

पुनरावृत्त-लोभी, ताबू, और RCPSP आधार-रेखाएँ

तीन प्रकाशित एकल-समाधान विधियाँ पूर्वता-सुरक्षित कार्य-क्रम स्थान में खोजती हैं, प्रत्येक अपने प्रामाणिक तंत्र को साझा क्रम-रन सीवन पर प्रक्षेपित करती है। serial-sgs-justification मंच का पहला संसाधन-परिबद्ध परियोजना-शेड्यूलिंग (RCPSP) सॉल्वर है।

Solver	तंत्र	प्रामाणिक संदर्भ
`iterated-greedy-rs`	विध्वंस-पुनर्निर्माण लूप और स्थिर-तापमान स्वीकृति सहित NEH बीज	Ruiz & Stutzle (2007)
`critical-path-tabu`	क्रांतिक-पथ ब्लॉक चालों पर उन्नत ताबू खोज	Nowicki & Smutnicki (2005)
`serial-sgs-justification`	दोहरे दायाँ-फिर-बायाँ औचित्यन सहित क्रमिक शेड्यूल-जनन-योजना विकूटन	Valls, Ballestin & Quintanilla (2005)

मेटाह्यूरिस्टिक आधार-रेखाएँ

प्रतिनिधि बीज-युक्त मेटाह्यूरिस्टिक आधार-रेखाएँ समान साध्यता, मरम्मत, अंकन, और स्थानीय-खोज संकारक साझा करती हैं, प्रत्येक अपने प्रामाणिक संदर्भ के साथ: आनुवंशिक एल्गोरिथम (Holland 1975), सिमुलेटेड एनीलिंग (Kirkpatrick et al. 1983), चींटी कॉलोनी (Dorigo, Maniezzo & Colorni 1996), कण समूह (Kennedy & Eberhart 1995), और अवकल विकास (Storn & Price 1997)।

कूटलिखित एडाप्टर सॉल्वर

व्यापक अधिगम कण समूह अनुकूलन और सफलता-इतिहास अनुकूली अवकल विकास प्रति कार्य एक घटक के साथ एक सतत, वास्तविक-मान सदिश खोजते हैं। प्रत्येक कोर एक सतत उद्देश्य पर सामान्य है, अतः इसका अभिसरण व्यवहार एक सतत बेंचमार्क पर सीधे सत्यापित होता है, और एक कूटलेखन एडाप्टर के माध्यम से शेड्यूलिंग समस्या से बँधा होता है जो एक सदिश को पूर्वता-साध्य कार्य क्रम में विकूटित करता है।

Solver	प्रेरणा	अनुकूलन	कूटलेखन
`clpso`	व्यापक अधिगम कण समूह (Liang et al. 2006)	पूर्वता क्रम में विकूटित सतत समूह	random-key
`d-clpso`	व्यापक अधिगम कण समूह (Liang et al. 2006)	सबसे-छोटे-स्थिति-मान विकूटन पर विविक्त एडाप्टर	spv
`lshade`	रैखिक जनसंख्या न्यूनन सहित सफलता-इतिहास अनुकूली DE (Tanabe & Fukunaga 2014)	पूर्वता क्रम में विकूटित सतत अवकल विकास	random-key
`d-lshade`	रैखिक जनसंख्या न्यूनन सहित सफलता-इतिहास अनुकूली DE (Tanabe & Fukunaga 2014)	पूर्णांक-रैंक पूर्णांकन पर विविक्त एडाप्टर	rounding

CLPSO एकल वैश्विक-श्रेष्ठ के बजाय एक व्यापक-अधिगम आदर्श से प्रत्येक विमा सीखता है, अतः समूह की श्रेष्ठ अनुकूलता एक एकल-शिखर द्रोणी पर एकदिष्ट रूप से सुधरती है। L-SHADE अपने सफलता इतिहास से क्रॉसओवर और पैमाना-कारक स्मृतियाँ अनुकूलित करता है और जनसंख्या को रैखिक रूप से न्यूनतम चार व्यक्तियों तक सिकोड़ता है। दोनों एक अभिसरण प्रक्षेपवक्र रिपोर्ट करते हैं, और एक अभिसरण-शुद्धता परीक्षण तब विफल होता है जब प्रेक्षित प्रक्षेपवक्र प्रकाशित संदर्भ व्यवहार का खंडन करता है, बीज-पुनरुत्पादनीयता जाँचों से अलग।

ये सॉल्वर एक कूटलेखन सेतु के माध्यम से एक विविक्त डोमेन के अनुकूलित सतत अनुकूलक हैं; वे किसी पूर्व कोड के सटीक पुनः-कार्यान्वयन नहीं हैं, और तुलनात्मक अभियानों के चलने से पहले कोई प्रदर्शन दावा नहीं किया जाता।

कूटलेखन एडाप्टर

प्रत्येक नामित सतत-से-विविक्त कूटलेखन एक स्पष्ट मरम्मत नीति वाला एक भिन्न एडाप्टर है, अतः कोई कूटलेखन एक सामान्य विकूटक में चुपचाप नहीं मुड़ता। प्रत्येक एडाप्टर अपने विकूटित क्रम को एक पूर्वता-साध्य क्रम में मरम्मत करता है और दर्ज करता है कि मरम्मत चली या नहीं।

एडाप्टर	अंतरण	विकूट नियम	उद्धरण स्थिति
`random-key`	तत्समक	क्लैंप की गई कुंजी से छँटाई	प्रामाणिक (Bean 1994)
`spv`	तत्समक	सबसे छोटा स्थिति मान	प्रामाणिक (Tasgetiren et al. 2007)
`rounding`	तत्समक	पूर्णांक रैंक खाँचे	अनुपयुक्त
`sigmoid`	S-आकार सिग्मॉइड	भारित प्रसंभाव्य चयन	प्रामाणिक (Kennedy & Eberhart 1997)
`v-shaped`	V-आकार परिमाण	भारित प्रसंभाव्य चयन	प्रामाणिक (Mirjalili & Lewis 2013)
`tanh`	विस्थापित अतिपरवलयिक स्पर्शज्या	भारित प्रसंभाव्य चयन	प्रामाणिक (Mirjalili & Lewis 2013)

नियतात्मक विकूट (random-key, spv, rounding) यादृच्छिक स्रोत की उपेक्षा करते हैं; अंतरण-फलन विकूट एक बीज-युक्त स्रोत का उपभोग करते हैं और एक स्थिर बीज के अंतर्गत पुनः-प्रस्तुत होते हैं। rounding एडाप्टर उद्धरण-अनुपयुक्त दर्ज करता है क्योंकि निकटतम-पूर्णांक रैंक पूर्णांकन एकल प्रामाणिक आद्य मूल रहित एक सामान्य विविक्तीकरण है।

विविधीकृत प्रतियोगी समुच्चय

विविधीकृत प्रतियोगी समुच्चय प्रतिनिधि आधार-रेखाओं से परे तुलना को कुछ हालिया, सशक्त सहकर्मियों के एक छोटे मुट्ठीभर से विस्तृत करता है, प्रत्येक एक नामित सॉल्वर अपने पूर्ण-पाठ संदर्भ के साथ केवल-क्रम निर्णय स्थान पर प्रक्षेपित, न कि शास्त्रीय आधार-रेखाओं की एक भारी सूची। एक सहकर्मी सार्वजनिक क्षेत्र पर केवल तभी रहता है जब वह एक स्थल-शक्ति पट्टी पार करता है: एक अनुक्रमित, सहकर्मी-समीक्षित स्थल में पश्च-2020 सामयिकता खिड़की के भीतर प्रकाशित, स्थल स्तर दर्ज के साथ ताकि एक पाठक उसे प्रतिष्ठा के बजाय साक्ष्य पर तौले।

Solver	तंत्र	स्थल	उद्धरण
`epso`	पथ संग्रहण सहित कार्यभार-पूर्वाग्रहित-प्रारंभीकरण समूह	Electronics (MDPI), 2023 — अनुक्रमित	Anbarkhan & Rakrouki (2023)
`adpso`	सफलता-अनुकूली अवरोही जड़त्व सहित समूह खोज	Sensors (MDPI), 2022 — अनुक्रमित	Nabi et al. (2022)
`ccgp`	प्राथमिकता-नियम वृक्षों का सहकारी सह-विकास	Computers & Operations Research (Elsevier), 2024 — शीर्ष-स्तरीय OR	Zaki et al. (2024)

प्रत्येक सहकर्मी अपने मेटाडेटा में शक्तियाँ, चेतावनियाँ, और एक चलाने-योग्य-आधार-रेखा साक्ष्य वर्ग धारण करता है ताकि अनुशंसक समझा सके कि एक सॉल्वर एक संदर्भ में क्यों फिट बैठता है। शास्त्रीय आधार-रेखाओं का एक व्यापक समुच्चय — पूर्णांक-कुंजी, पूर्वाग्रहित-यादृच्छिक-कुंजी, और वितरण-आकलन आनुवंशिक एल्गोरिथम, और बड़ी-पड़ोस, पुनरावृत्त लोभी, ताबू, चर-पड़ोस, और मीमेटिक खोजें — आंतरिक तुलना के लिए पंजीकृत रहता है पर सार्वजनिक क्षेत्र से बाहर रखा जाता है, क्योंकि प्रतिनिधि अनुमानिकाएँ पहले से अपना तंत्र संकेत धारण करती हैं। तुलनात्मक अभियानों के चलने से पहले कोई प्रदर्शन दावा नहीं किया जाता।

सामयिकता लंगर

प्रतियोगी और संबंधित-कार्य समुच्चय एक पश्च-2020 सामयिकता लंगर के माध्यम से वर्तमान-दशक के कार्य के विरुद्ध स्थापित होता है: Karimi-Mamaghan, Mohammadi, Pasdeloup, और Meyer (2023, क्रमचय फ़्लोशॉप के लिए पुनरावृत्त लोभी में एकीकृत Q-learning के माध्यम से संकारक चुनना सीखना, European Journal of Operational Research 304(3):1296-1330, doi:10.1016/j.ejor.2022.03.054) और वितरित फ़्लोशॉप समूह शेड्यूलिंग के लिए 2024 IEEE Transactions on Evolutionary Computation का सूचक-चालित सहयोगी बहु-उद्देश्य विकासवादी एल्गोरिथम (doi:10.1109/TEVC.2023.3339558)।

बहु-उद्देश्य प्रतियोगी

दो Pareto प्रतियोगी एक बहु-उद्देश्य सदिश पर पूर्वता-सुरक्षित कार्य क्रमों की एक जनसंख्या विकसित करते हैं, जो अनेक-उद्देश्य nsga3 सतह से भिन्न है। nsga2 प्रभुत्व-आधारित प्रतियोगी है — तेज़ अप्रभुत्वित छँटाई सहित भीड़-दूरी टाई-तोड़ — और moead अपघटन-आधारित प्रतिभार है, जो समस्या को एक संरचित सिंप्लेक्स भार ग्रिड के अनुदिश अदिश Tchebycheff उप-समस्याओं में विभाजित करता है और प्रत्येक उप-समस्या के निकटतम-भार पड़ोस में अधिष्ठित को प्रतिस्थापित करता है।

Solver	तंत्र	प्रामाणिक संदर्भ
`nsga2`	प्रभुत्व-आधारित Pareto: तेज़ अप्रभुत्वित छँटाई, भीड़ दूरी	Deb et al. (2002)
`moead`	अपघटन-आधारित Pareto: सिंप्लेक्स भार ग्रिड पर Tchebycheff अदिशीकरण	Zhang & Li (2007)

अनेक-उद्देश्य प्रतियोगी

nsga3 नामित अनेक-उद्देश्य प्रतियोगी है (Deb & Jain 2014, doi:10.1109/TEVC.2013.2281535), द्वि-उद्देश्य प्रतियोगी सतह से भिन्न। यह पूर्वता-सुरक्षित कार्य क्रमों पर एक जनसंख्या बनाए रखता है, प्रत्येक क्रम को एक बहु-उद्देश्य सदिश (makespan, विलंबता, और भार निष्पक्षता) पर मूल्यांकित करता है, और इकाई सिंप्लेक्स पर Das & Dennis संरचित संदर्भ बिंदुओं पर संदर्भ-बिंदु-नीचिंग चयन द्वारा प्रत्येक पीढ़ी जीवित रहता है। संदर्भ-बिंदु डिज़ाइन, समाधानों का उनके निकटतम संदर्भ दिशा से सहचारण, और नीच-गणना चयन एल्गोरिथमी हस्ताक्षर हैं; सॉल्वर multi-objective के साथ many-objective क्षमता घोषित करता है ताकि एक अभियान उसे स्पष्ट रूप से चुन सके।

क्षमता मिलान

SolverRegistry.select क्षमता, बाधा, और उद्देश्य द्वारा सॉल्वरों का मिलान करता है। क्षमता और बाधा आवश्यकताएँ दोनों क्षमता टैगों के रूप में व्यक्त होती हैं और एक संयोजन के रूप में मिलान की जाती हैं: एक अनेक-उद्देश्य अभियान जिसे deadline-सजगता भी चाहिए required_capabilities=(SolverCapability.MANY_OBJECTIVE,) और required_constraints=(SolverCapability.DEADLINE_AWARE,) पास करता है, और रजिस्ट्री केवल वे सॉल्वर लौटाती है जो दोनों घोषित करते हैं। एक objective फ़िल्टर परिणाम को आगे उन सॉल्वरों तक सीमित करता है जो उस नामित उद्देश्य के लिए समर्थन घोषित करते हैं।

NDSO परिवार

NDSO परिवार रजिस्ट्री का मूल-कूटलेखन सॉल्वर परिवार है: यह साध्य शेड्यूलों पर सीधे खोजता है। यह जो प्रत्येक शेड्यूल उत्पन्न करता है वह निर्माण द्वारा साध्य है (एक वैधता-द्वारा-डिज़ाइन निर्माता प्रत्येक चरण पर एक पूर्वता-सम्मान क्रम बनाता है), अतः परिवार native कूटलेखन धारण करता है और कभी एक कूट/विकूट चरण या मरम्मत पास नहीं चलाता। परिवार नामित तंत्रों के एक छोटे समुच्चय की रचना करता है:

विश्वास आव्यूह — प्रति (स्थिति, कार्य) कोष्ठिका सीखे गए भरोसे का एक विरल भंडार, जैसे ही बेहतर शेड्यूल अपनी कोष्ठिकाओं को प्रबलित करते हैं अद्यतन होता है।
विश्वास-भारित मतदान — विश्वास आव्यूह द्वारा भारित जनसंख्या पर मतदान करके अभिजात शेड्यूल संश्लेषित करता है; तेज़ रूपांतर इसके बजाय एक अ-भारित बहुमत मत उपयोग करता है।
मात्रा-और-गुणवत्ता शेड्यूल — मात्रा शेड्यूल निर्धारित करता है कि एक उम्मीदवार कितना बदलता है; गुणवत्ता शेड्यूल निर्धारित करता है कि यह किस मार्गदर्शन स्रोत से सीखता है।
तीन-स्रोत मार्गदर्शन — एक उम्मीदवार संश्लेषित अभिजात (दोहन), एक सहकर्मी (विविधता), या एक लुप्त-ज्ञान स्रोत (आमूल अन्वेषण) से सीखता है।
एकीकृत अनुकूली गुणांक — एक अ-रैखिक शेड्यूल परिवार को अन्वेषण से दोहन की ओर खिसकाता है और संवेदनशीलता तथा अधिगम-केंद्र दोनों को चालित करता है; तेज़ रूपांतर इसे एक स्थिर मान पर फिक्स करता है।

रूपांतर	रचना
`ndso-core`	विश्वास-भारित मतदान, अनुकूली गुणांक, तीन-स्रोत मार्गदर्शन
`ndso-fast`	बहुमत-मत संश्लेषण, स्थिर गुणांक, एकल मार्गदर्शन स्रोत
`ndso-summit`	व्यापक संश्लेषण सहित कई समूहों का समन्वय करने वाली अंतर-समूह परिषद

अंतर-समूह परिषद

ndso-summit रूपांतर गुणवत्ता रचना है: यह कई समूहों को समानांतर में चलाता है और उन्हें एक परिषद के माध्यम से समन्वित करता है। प्रत्येक समूह अपनी स्वयं की जनसंख्या पर कोर तंत्रों की रचना करता है; अंतर-समूह परिषद उन समूहों को कई पृथक रनों के बजाय एक खोज के रूप में काम करते रखती है और उनके परिणामों को एक एकल व्यापक अभिजात — वह शेड्यूल जिसके पीछे पूरा शिखर खड़ा है — में संश्लेषित करती है। परिषद वही है जो रूपांतर को एक नज़र में अलग करती है: ndso-core और ndso-fast प्रत्येक एक जनसंख्या से खोजते हैं, जबकि ndso-summit समन्वित बहु-समूह खोज के लिए निर्मित रचना है।

परिषद का समन्वय व्यवहार और प्रति-समूह निदान विन्यास-योग्य हैं, और यह जो प्रत्येक शेड्यूल उत्पन्न करता है वह निर्माण द्वारा साध्य रहता है।

विलोपन मानचित्र

विलोपन मानचित्र प्रत्येक नामित तंत्र के लिए एक पृथक्कारी विन्यास गणित करता है ताकि अनुप्रवाह विश्लेषण प्रत्येक तंत्र का योगदान आरोपित कर सके। अंतर-समूह प्रविष्टियाँ प्रत्येक एक कोर स्विच — विश्वास आव्यूह, विश्वास-भारित मतदान, मात्रा शेड्यूल, गुणवत्ता शेड्यूल, बहु-स्रोत मार्गदर्शन संरचना, सहकर्मी स्रोत, लुप्त-ज्ञान स्रोत, और अनुकूली गुणांक — को निष्क्रिय करती हैं। समन्वय प्रविष्टियाँ प्रत्येक एक परिषद पैरामीटर — अंतःसमूह- बनाम अंतर-समूह समन्वय, और व्यापक संश्लेषण — को निष्क्रिय करती हैं। प्रत्येक प्रविष्टि उस रन-गणना तल को घोषित करती है जिस पर विश्लेषण परत अपनी तुलना नमूना लेती है और उन सांख्यिकीय परीक्षणों को नामित करती है जो वह परत लागू करती है (एक युग्मित अ-पैरामीट्रिक सार्थकता परीक्षण, Holm बहु-तुलना सुधार सहित एक Friedman औसत-रैंक पश्च-हॉक, और एक Cliff-delta प्रभाव आकार)। नमूना-ली गई तुलना अनुप्रवाह प्रयोग इंजन द्वारा भौतिकीकृत होती है; प्रक्रिया-अंदर runner सिद्ध करता है कि प्रत्येक पृथक्करण साध्य है।

एक चरणबद्ध निर्यात फ़िल्टर शासित करता है कि एक रिपोर्ट गुंजाइश किन तंत्रों को उजागर कर सकती है: आधार गुंजाइश केवल आधारभूत तंत्र उजागर करती है, और एक fail-closed गेट एक आधार गुंजाइश के अंतर्गत एक अधिक-तेज़ या उच्चतर-गुणवत्ता तंत्र को रिसाने के बजाय उठाता है, अतः आधारभूत और तेज़ गुंजाइशें केवल-परिषद तंत्रों को उजागर नहीं कर सकतीं। अभिसरण निदान — जनसंख्या विविधता, विश्वास-आव्यूह एन्ट्रॉपी, अन्वेषण अनुपात, संसाधन भार, समयन, और सुधार अनुरेखण — वैकल्पिक हैं और निष्क्रिय होने पर कोई ओवरहेड नहीं जोड़ते, और परिषद एक JSON मैनिफ़ेस्ट निर्यात करती है जिसे विश्लेषण परत किसी सॉल्वर प्रकार को आयात किए बिना अंतर्ग्रहण करती है।

सटीक एडाप्टर

सटीक एडाप्टर दो रूपों में से एक लेते हैं। वैकल्पिक-बैकएंड एडाप्टर एक वैकल्पिक निर्भरता घोषित करते हैं और रनटाइम पर उसका आयात मूल जाँचते हैं; यदि बैकएंड अनुपस्थित है तो वे पैकेज आयात के दौरान एक भारी निर्भरता आयात करने के बजाय extra नाम, उद्देश्य, वाणिज्यिक ध्वज, और लाइसेंसिंग टिप्पणी सहित MissingOptionalDependencyError उठाते हैं। मूल-परिबद्ध एडाप्टर कोई तृतीय-पक्ष निर्भरता नहीं धारण करते और अपनी स्वयं की परिबद्ध खोज से छोटी इंस्टेंसों को सटीक हल करते हैं — एक स्थिति में पूर्वता-साध्य क्रमों का गणन, दूसरी में निर्णय-आरेख शाखन और परिबंधन —, इंस्टेंस के समर्थित कार्य गणना से अधिक होने पर UnsupportedCapabilityError उठाते हैं।

Solver	विधि	बैकएंड	Extra	वाणिज्यिक
`ortools-cp-sat`	CP-SAT	`ortools`	`exact`	नहीं
`pulp-milp`	MILP / MIP	`pulp`	`exact`	नहीं
`branch-and-bound`	branch-and-bound / cut	`ortools`	`exact`	नहीं
`logic-based-benders-decomposition`	तर्क-आधारित बेंडर्स अपघटन	`pulp`	`exact`	नहीं
`exhaustive-enumeration`	संपूर्ण गणन	मूल (कोई बैकएंड नहीं)	—	नहीं
`decision-diagram-sequencing`	निर्णय-आरेख शाखन और परिबंधन	मूल (कोई बैकएंड नहीं)	—	नहीं
`gurobi-exact`	MILP / MIP	`gurobipy`	`exact-commercial`	हाँ

सार्वजनिक सटीक मुट्ठीभर प्रतिनिधि खुला समुच्चय है — CP-SAT, MILP, branch-and-bound, और तर्क-आधारित बेंडर्स अपघटन — साथ ही दो मूल परिबद्ध सॉल्वर और वास्तव में आह्वान करने वाला Gurobi आवरण। वाणिज्यिक Gurobi आवरण exact-commercial extra के पीछे रहता है, कभी बंडल नहीं होता, और एक स्पष्ट लाइसेंसिंग टिप्पणी धारण करता है; एक शैक्षणिक लाइसेंस उपलब्ध है। वैकल्पिक बैकएंडों का एक व्यापक पूल आंतरिक उपयोग के लिए पंजीकृत रहता है।

चयन और अधिगम परत

चयन परत एक वैश्विक-श्रेष्ठ सॉल्वर का कभी दावा किए बिना एक समस्या के लिए सॉल्वरों को रैंक करती है। यह एक SelectionFeatures अभिलेख — difficulty, heterogeneity, objective_conflict, uncertainty, dynamism, और solver_sensitivity — और सार्वजनिक सॉल्वर मेटाडेटा का उपभोग करती है, और रैंक की गई SolverRecommendation पंक्तियाँ लौटाती है। प्रत्येक अनुशंसा एक RecommendationSource (metadata या learned-model), एक ConfidenceLabel, और स्पष्ट सीमा टिप्पणियाँ धारण करती है, अतः एक मेटाडेटा अनुशंसा कभी एक सीखी गई के रूप में गलत नहीं समझी जाती।

FEATURE_ORIGIN प्रत्येक विशेषता को उस बेंचमार्क अभिलक्षण मीट्रिक तक अनुरेखित करता है जिसे वह पढ़ता है; उपभोक्ता dispatchatlas.bench अभिलक्षण मीट्रिकों को विशेषता अनुबंध में मानचित्रित करता है, अतः dispatchatlas.solve अब भी केवल dispatchatlas.core आयात करता है। दो चयनकर्ता भेजे जाते हैं: RuleBasedSelector केवल घोषित क्षमताओं और अभिलक्षण से रैंक करता है (एक मेटाडेटा अनुशंसा), और SupervisedSelector एक लेबल किए गए कॉर्पस से एक नियतात्मक दूरी-भारित निकटतम-पड़ोसी मॉडल द्वारा रैंक करता है (एक सीखे-मॉडल अनुशंसा)।

पर्यवेक्षित चयनकर्ता प्रशिक्षण फिट नहीं बल्कि होल्ड-आउट सामान्यीकरण रिपोर्ट करता है। प्रति-सत्यापन प्रोटोकॉल एक लेबल किए गए कॉर्पस को इस प्रकार विभाजित करता है कि कोई इंस्टेंस, कोई बेंचमार्क परिवार, और कोई अभिलक्षण अभिलेख प्रशिक्षण और परीक्षण दोनों विभाजनों में प्रकट न हो (partition_by_families, leave_one_family_out, leakage_report), शेष परिवारों पर प्रशिक्षित करता है, और होल्ड-आउट परिवार को अंकित करता है (held_out_generalization, cross_validate)। इसका विश्वास केवल तभी केवल-मेटाडेटा से ऊपर उठता है जब एक रिसाव-मुक्त होल्ड-आउट मूल्यांकन इसे समर्थन देता है।

learning_interface_catalog() सात नामित अधिगम और संकर अंतरफलक पंजीकृत करता है — पर्यवेक्षित एल्गोरिथम चयन, प्रतिनिधि-सहायित खोज, सुदृढीकरण-अधिगम हुक, हाइपर-ह्यूरिस्टिक्स, नीति-निर्देशित मरम्मत, सीखा गया प्रारंभीकरण, और एक केवल-बेंचमार्क आधार-रेखा। प्रत्येक एक LearningEvidencePolicy (प्रशिक्षण डेटा, रिसाव नियंत्रण, पुनरुत्पादनीयता, साक्ष्य वर्ग, साक्ष्य-श्रेणी पात्रता) घोषित करता है। दो यहाँ कार्यान्वित हैं; अन्य पाँच पंजीकृत आस्थगित अंतरफलक हैं जिनका साकारण प्रशिक्षण डेटा उत्पन्न करने वाले तुलनात्मक अभियानों पर गेट किया गया है। भारी आकलक बैकएंड वैकल्पिक learning extra के पीछे रहते हैं और आयात मूल द्वारा जाँचे जाते हैं, कभी पैकेज आयात के दौरान आयात नहीं होते; अनुपस्थिति MissingOptionalDependencyError उठाती है जबकि नियतात्मक फ़ॉलबैक उपलब्ध रहता है।

प्रदर्शन रक्षोपाय

बैच अंकन BatchScoringProfile के माध्यम से स्पष्ट है। वर्तमान कर्नेल एक मानक-पुस्तकालय फ़ॉलबैक उपयोग करता है और उस फ़ॉलबैक को निदान में दर्ज करता है। यह API को सदिशीकृत या त्वरित कर्नेलों के लिए तैयार रखता है जबकि एक परीक्षित, सुवाह्य पथ संरक्षित करता है।