Solving Type-2 Fuzzy Distributed Hybrid Flowshop Scheduling Using an Improved Brain Storm Optimization Algorithm

Li, Junqing; Li, Jiake; Zhang, Lijing; Sang, Hongshi; Han, Yuyan; Chen, Qingda

doi:10.1007/s40815-021-01050-9

Cited by 24 publications

(5 citation statements)

References 69 publications

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“…Du et al used a learn-to-improve reinforcement learning approach, which is a combination of three coordinated double deep Q-networks to solve the distributed scheduling problem in precast concrete production [35]. Li et al presented an improved brainstorm optimization algorithm to optimize the fuzzy complication time [36].…”

Section: Distributed Hybrid Flowshop Schedulingmentioning

confidence: 99%

A dimension-aware gaining-sharing knowledge algorithm for distributed hybrid flowshop scheduling with resource-dependent processing time

Li,

et al. 2024

Complex Intell. Syst.

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The resource-assisted processing operation involves the coupling of multi-dimensional sub-problem, which poses a challenge in scheduling system. In this study, a dimension-aware gain-sharing knowledge algorithm (DGSK) is presented to address the distributed hybrid flowshop scheduling problem with resource-dependent processing times (DHFSP-RDPT), where the makespan is to be minimized. Firstly, by analyzing the mathematical model of the DHFSP-RDPT, four problem-specific lemmas and two novel resource reallocation rules are proposed. The DGSK begin with a high-performance initial population, which is generated by three knowledge-driven heuristics in hybrid way. Next, a discrete evolution-based search mechanism assists the DGSK to extend the search in solution space. Furthermore, a dimension-aware two-stage local search combined with meta-Lamarckian learning method is embedded to enhance the local search ability for the multidimensional problems. Finally, the proposed algorithm is measured on a series of instances based on real production data. The results demonstrate that the DGSK improves the performance by in solving DHFSP-RDPT compared to the state-of-the-art methods.

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Section: Distributed Hybrid Flowshop Schedulingmentioning

confidence: 99%

A dimension-aware gaining-sharing knowledge algorithm for distributed hybrid flowshop scheduling with resource-dependent processing time

Li,

et al. 2024

Complex Intell. Syst.

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“…In other words, if task j is executed, none of the tasks in J C j can be executed. Constraint (6) ensures that a task can be executed only if its preceding task has been executed earlier than it. Constraint (7) calculates the switching time between two immediately adjacent tasks j and j ′ .…”

Section: Decision Variablesmentioning

confidence: 99%

“…Motivated by this aim, this paper studies a novel linear disassembly line balancing problem, additionally considering the hazardous disassembly penalties and switching time (LDPHS), which has never been studied in previous work. It aims to disassemble parts with high recycling value [4][5][6][7] and reduce disassembly risk due to hazardous components. Although some existing studies have dealt with hazardous parts in disassembly line balancing problems [8,9], they usually consider removing them earlier but not their specific penalties.…”

Section: Introductionmentioning

confidence: 99%

Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time

Zhao,

Xiao,

Wang

et al. 2023

Mathematics

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Disassembling discarded electrical products plays a crucial role in product recycling, contributing to resource conservation and environmental protection. While disassembly lines are progressively transitioning to automation, manual or human–robot collaborative approaches still involve numerous workers dealing with hazardous disassembly tasks. In such scenarios, achieving a balance between low risk and high revenue becomes pivotal in decision making for disassembly line balancing, determining the optimal assignment of tasks to workstations. This paper tackles a new disassembly line balancing problem under the limitations of quantified penalties for hazardous component disassembly and the switching time between adjacent tasks. The objective function is to maximize the overall profit, which is equal to the disassembly revenue minus the total cost. A mixed-integer linear program is formulated to precisely describe and optimally solve the problem. Recognizing its NP-hard nature, a metaheuristic algorithm, inspired by human idea generation and population evolution processes, is devised to achieve near-optimal solutions. The exceptional performance of the proposed algorithm on practical test cases is demonstrated through a comprehensive comparison involving its solutions, exact solutions obtained using CPLEX to solve the proposed mixed-integer linear program, and those of competitive peer algorithms. It significantly outperforms its competitors and thus implies its great potential to be used in practice. As computing power increases, the effectiveness of the proposed methods is expected to increase further.

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“…Ying and Lin [47] studied distributed HFSP with multiprocessor tasks and presented a self-tuning iterated greedy (SIG) to minimize makespan. Li et al [48] developed an improved artificial bee colony (IABC) for distributed HFSP with SDST and unrelated parallel machines. The proposed IABC adopts the factory assignment rule, the greedy iterative strategy, and a hybrid search strategy.…”

Section: Instance and Comparative Algorithmsmentioning

confidence: 99%

Q-Learning-Based Teaching-Learning Optimization for Distributed Two-Stage Hybrid Flow Shop Scheduling with Fuzzy Processing Time

Lei

2022

Complex Syst. Model. Simul.

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Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings.However, the distributed two-stage hybrid flow shop scheduling problem (DTHFSP) with fuzzy processing time is seldom investigated in multiple factories. Furthermore, the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP. In the current study, DTHFSP with fuzzy processing time was investigated, and a novel Q-learning-based teaching-learning based optimization (QTLBO) was constructed to minimize makespan. Several teachers were recruited for this study. The teacher phase, learner phase, teacher's self-learning phase, and learner's self-learning phase were designed. The Q-learning algorithm was implemented by 9 states, 4 actions defined as combinations of the above phases, a reward, and an adaptive action selection, which were applied to dynamically adjust the algorithm structure. A number of experiments were conducted. The computational results demonstrate that the new strategies of QTLBO are effective; furthermore, it presents promising results on the considered DTHFSP.

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Solving Type-2 Fuzzy Distributed Hybrid Flowshop Scheduling Using an Improved Brain Storm Optimization Algorithm

Cited by 24 publications

References 69 publications

A dimension-aware gaining-sharing knowledge algorithm for distributed hybrid flowshop scheduling with resource-dependent processing time

A dimension-aware gaining-sharing knowledge algorithm for distributed hybrid flowshop scheduling with resource-dependent processing time

Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time

Q-Learning-Based Teaching-Learning Optimization for Distributed Two-Stage Hybrid Flow Shop Scheduling with Fuzzy Processing Time

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