A Cooperative Scatter Search With Reinforcement Learning Mechanism for the Distributed Permutation Flowshop Scheduling Problem With Sequence-Dependent Setup Times

Zhao, Fuqing; Zhou, Gang

doi:10.1109/tsmc.2023.3256484

Cited by 23 publications

(3 citation statements)

References 50 publications

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“…[43], a bi-population cooperative framework based on double Q-learning was designed to further optimize distributed no-wait flow shop scheduling problem. Li et al [44] combined artificial bee colony algorithm and Q-learning to solve the permutation flow shop scheduling problem with minimizing the makespan. A cooperative scatter search with Q-learning mechanism (QCSS) was presented in Ref.…”

Section: Comentioning

confidence: 99%

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

Deng,

Zhu,

et al. 2024

Complex Syst. Model. Simul.

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There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling, but most scholars neglect the connection between them, which means the purpose of both models is to simulate a more realistic factory environment. From this perspective, the solutions can be more precise and practical if both issues are considered simultaneously. Therefore, the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper, which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time. Apart from that, many other contributions can be stated as follows. A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm (RB 2 EA) is proposed, which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population. A local enhancement method which combimes multiple local search stratgies is presented. An interaction mechanism is designed to promote the convergence of the bi-population. Extensive experiments are designed to evaluate the efficacy of RB 2 EA, and the conclusion can be drew that RB 2 EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs (EFFJSPD) efficiently.

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Section: Comentioning

confidence: 99%

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

Deng,

Zhu,

et al. 2024

Complex Syst. Model. Simul.

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“…Meta-heuristics are prone to local optimum and have low convergence speed. As one of the most commonly used reinforcement learning algorithms, Q-learning has been employed to improve the performance of meta-heuristics in recent years [28][29][30][31]. Ren et al [32] proposed a variable neighborhood search algorithm with Q-learning to solve disassembly line scheduling problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving Heterogeneous USV Scheduling Problems by Problem-Specific Knowledge Based Meta-Heuristics with Q-Learning

Ma,

Gao,

et al. 2024

Mathematics

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This study focuses on the scheduling problem of heterogeneous unmanned surface vehicles (USVs) with obstacle avoidance pretreatment. The goal is to minimize the overall maximum completion time of USVs. First, we develop a mathematical model for the problem. Second, with obstacles, an A* algorithm is employed to generate a path between two points where tasks need to be performed. Third, three meta-heuristics, i.e., simulated annealing (SA), genetic algorithm (GA), and harmony search (HS), are employed and improved to solve the problems. Based on problem-specific knowledge, nine local search operators are designed to improve the performance of the proposed algorithms. In each iteration, three Q-learning strategies are used to select high-quality local search operators. We aim to improve the performance of meta-heuristics by using Q-learning-based local search operators. Finally, 13 instances with different scales are adopted to validate the effectiveness of the proposed strategies. We compare with the classical meta-heuristics and the existing meta-heuristics. The proposed meta-heuristics with Q-learning are overall better than the compared ones. The results and comparisons show that HS with the second Q-learning, HS + QL2, exhibits the strongest competitiveness (the smallest mean rank value 1.00) among 15 algorithms.

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“…These models prove to be effective in addressing the complexities of DHFSP-SDST in various manufacturing settings. A cooperative scatter search algorithm incorporating Q-learning is presented to tackle the DPFSP-SDST [16]. Yu et al [17] design three constructive heuristics to solve the DPFSP-SDST.…”

mentioning

confidence: 99%

Ensemble evolutionary algorithms equipped with Q‐learning strategy for solving distributed heterogeneous permutation flowshop scheduling problems considering sequence‐dependent setup time

Liu,

Gao,

et al. 2024

IET Collab Intel Manufact

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A distributed heterogeneous permutation flowshop scheduling problem with sequence‐dependent setup times (DHPFSP‐SDST) is addressed, which well reflects real‐world scenarios in heterogeneous factories. The objective is to minimise the maximum completion time (makespan) by assigning jobs to factories, and sequencing them within each factory. First, a mathematical model to describe the DHPFSP‐SDST is established. Second, four meta‐heuristics, including genetic algorithms, differential evolution, artificial bee colony, and iterated greedy (IG) algorithms are improved to optimally solve the concerned problem compared with the other existing optimisers in the literature. The Nawaz‐Enscore‐Ham (NEH) heuristic is employed for generating an initial solution. Then, five local search operators are designed based on the problem characteristics to enhance algorithms' performance. To choose the local search operators appropriately during iterations, Q‐learning‐based strategy is adopted. Finally, extensive numerical experiments are conducted on 72 instances using 5 optimisers. The obtained optimisation results and comparisons prove that the improved IG algorithm along with Q‐learning based local search selection strategy shows better performance with respect to its peers. The proposed algorithm exhibits higher efficiency for scheduling the concerned problems.

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A Cooperative Scatter Search With Reinforcement Learning Mechanism for the Distributed Permutation Flowshop Scheduling Problem With Sequence-Dependent Setup Times

Cited by 23 publications

References 50 publications

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

Solving Heterogeneous USV Scheduling Problems by Problem-Specific Knowledge Based Meta-Heuristics with Q-Learning

Ensemble evolutionary algorithms equipped with Q‐learning strategy for solving distributed heterogeneous permutation flowshop scheduling problems considering sequence‐dependent setup time

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