A review and classification on distributed permutation flowshop scheduling problems

European Journal of Operational Research, Год журнала: 2023, Номер 312(1), С. 1 - 21

Опубликована: Фев. 5, 2023

The Distributed Permutation Flowshop Scheduling (DPFS) problem is one of the fastest-growing topics in scheduling literature, which turn among most prolific fields Operational Research (OR). Although has been formally stated only twelve years ago, number papers on topic growing at a rapid pace, and rising interest –both from academics practitioners– distributed manufacturing paradigms seems to indicate that this trend will continue increase. Possibly as side effect steady growth, state-of-the-art many decision problems within field far being clear, with substantial overlaps solution procedures, lack (fair) comparisons against existing methods, or use different denominations for same problem, other issues. In paper, we carry out review DPFS literature aimed providing classification notation under common framework. Within framework, contributions are exhaustively presented discussed, together lines future research.

Язык: Английский

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A novel Q-learning based variable neighborhood iterative search algorithm for solving disassembly line scheduling problems

Swarm and Evolutionary Computation, Год журнала: 2023, Номер 80, С. 101338 - 101338

Опубликована: Май 24, 2023

Язык: Английский

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An Iterative Greedy Algorithm With Q-Learning Mechanism for the Multiobjective Distributed No-Idle Permutation Flowshop Scheduling

IEEE Transactions on Systems Man and Cybernetics Systems, Год журнала: 2024, Номер 54(5), С. 3207 - 3219

Опубликована: Фев. 13, 2024

The distributed no-idle permutation flowshop scheduling problem (DNIPFSP) has widely existed in various manufacturing systems. makespan and total tardiness are optimized simultaneously considering the variety of scales problems with introducing an improved iterative greedy (IIG) algorithm. variable neighborhood descent (VND) algorithm is applied to local search method Two perturbation operators based on critical factory proposed as structure VND. In destruction phase, scale varies size problem. An insertion operator-based strategy sorts undeleted jobs after phase. $Q$ -learning mechanism for selecting weighting coefficients introduced obtain a relatively small objective value. Finally, tested benchmark suite compared other existing algorithms. experiments show that IIG obtained more satisfactory results.

Язык: Английский

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A Pareto-Based Discrete Jaya Algorithm for Multiobjective Carbon-Efficient Distributed Blocking Flow Shop Scheduling Problem

IEEE Transactions on Industrial Informatics, Год журнала: 2022, Номер 19(8), С. 8588 - 8599

Опубликована: Ноя. 9, 2022

Carbon peaking and carbon neutrality, which are significant strategies for national sustainable development, have attracted enormous attention from researchers in the manufacturing domain. A Pareto-based discrete Jaya algorithm (PDJaya) is proposed to solve carbon-efficient distributed blocking flow shop scheduling problem (CEDBFSP) with criteria of total tardiness emission this article. The mixed-integer linear programming model presented CEDBFSP. An effective constructive heuristic produced generate initial population. new individual generated by update mechanism PDJaya. self-adaptive operator local search strategy designed enhance exploitation capability critical-path-based saving introduced further reduce emissions. effectiveness each PDJaya verified compared state-of-the-art algorithms benchmark suite. numerical results demonstrate that efficient optimizer solving

Язык: Английский

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A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem

IEEE Transactions on Industrial Informatics, Год журнала: 2022, Номер 19(5), С. 6692 - 6705

Опубликована: Июль 21, 2022

This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP), which has important applications in manufacturing systems. The objective is to minimize the total flowtime. A mixed-integer linear programming model of DANWFSP with flowtime criterion proposed. population-based iterated greedy algorithm (PBIGA) presented address problem. new constructive heuristic generate an initial population high quality. For DANWFSP, accelerated NR3 proposed assign jobs factories, improves efficiency and saves CPU time. To enhance effectiveness PBIGA, local search method destruction-construction mechanisms are designed for product sequence job sequence, respectively. selection mechanism determine, individuals execute method. An acceptance determine whether offspring adopted by population. Finally, PBIGA seven state-of-the-art algorithms tested on 810 large-scale benchmark instances. experimental results show that effective performs better than recently compared this article.

Язык: Английский

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LS-HH: A Learning-Based Selection Hyper-Heuristic for Distributed Heterogeneous Hybrid Blocking Flow-Shop Scheduling

IEEE Transactions on Emerging Topics in Computational Intelligence, Год журнала: 2022, Номер 7(1), С. 111 - 127

Опубликована: Май 25, 2022

As the development of economic globalization, distributed manufacturing has become common in modern industries. The scheduling production resources multiple centers becomes an emerging topic. This paper is first attempt to address a heterogeneous hybrid blocking flow-shop problem (DHHFSP-B) with minimization makespan. Compared traditional single scheduling, DHHFSP-B considers collaborative flow lines layout and processing performance as well no intermediate buffers. We firstly present mixed-integer linear programming model formulate then propose learning-based selection hyper-heuristic framework (LS-HH) for solving it. LS-HH contains high-level strategy low-level heuristics. In strategy, learning probability built provide guidance choose suitable perturbation heuristic during optimization process. A simulated annealing-like move acceptance employed determine updating incumbent domain solution prevent search from trapping into local optimum. heuristics, constructive proposed based on novel assignment rule create initial solution. Four problem-specific heuristics variable neighborhood search-based improvement operator are space. comprehensive computational experiment conducted. comparative results show that significantly outperforms Gurobi solver several closely relevant methods DHHFSP-B.

Язык: Английский

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Toward automated algorithm configuration for distributed hybrid flow shop scheduling with multiprocessor tasks

Knowledge-Based Systems, Год журнала: 2023, Номер 264, С. 110309 - 110309

Опубликована: Янв. 20, 2023

Язык: Английский

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Problem-specific knowledge MOEA/D for energy-efficient scheduling of distributed permutation flow shop in heterogeneous factories

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 123, С. 106454 - 106454

Опубликована: Май 25, 2023

Язык: Английский

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A cooperative population-based iterated greedy algorithm for distributed permutation flowshop group scheduling problem

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 125, С. 106750 - 106750

Опубликована: Июль 17, 2023

Язык: Английский

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A Reinforcement Learning Driven Artificial Bee Colony Algorithm for Distributed Heterogeneous No-Wait Flowshop Scheduling Problem With Sequence-Dependent Setup Times

IEEE Transactions on Automation Science and Engineering, Год журнала: 2022, Номер 20(4), С. 2305 - 2320

Опубликована: Ноя. 4, 2022

The distributed heterogeneous factory system is a typical scenario in the manufacturing industry. A no-wait flowshop scheduling problem with sequence-dependent setup times (DHNWFSP-SDST) studied this paper. differences configuration and transportation time are considered DHNWFSP-SDST. mixed-integer linear programming (MILP) model constructed an artificial bee colony algorithm (ABC) Q-learning (QABC) proposed to address Heuristic methods named NEH_H DHHS designed construct potential initial candidates for population. neighborhood structures based on job blocks introduced QABC explore solution space during evolution processes. mechanism employed select via empirical knowledge operation speed-up accelerate evaluation of obtained reduce computation QABC. experimental results show that Note Practitioners—Distributed under environment generally exists real systems. refers reasonable arrangement production orders optimize certain indicators limited time, resources, computing costs. Distributed important industrial takes into account regions. kind NP-hard as huge. reinforcement learning driven problem. utilized solutions. Neighborhood further solution. effective guidance provided by selection structure avoid invalid search. obtains high-quality scheme time.

Язык: Английский

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