Application research of a new neighbourhood structure with adaptive genetic algorithm for job shop scheduling problem

Liang, Zhongyuan; Liu, Mei; Zhong, Peisi; Zhang, Chao

doi:10.1080/00207543.2021.2007310

Cited by 10 publications

(2 citation statements)

References 36 publications

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“…The multiple resource-constrained job shop scheduling problem constraints are widely regarded as a typical NP-Hard problem. In tackling this formidable challenge, researchers frequently resort to employing metaheuristic algorithms, including the genetic algorithm [30,31], grey wolf optimization algorithms [32] and particle swarm algorithms [33], differential evolution algorithm [34]. Specifically, the FJSP-MRST presents a comprehensive problem that encompasses mold changeover time, machine, and mold resource constraints, thus manifesting as an evident NP-hard problem.…”

Section: Multi-objective Differential Evolutionary Algorithmmentioning

confidence: 99%

Flexible Job Shop Scheduling Method for Optimizing Mold Resource Setup Time

Cheng,

Xie,

Xin

et al. 2024

IEEE Access

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In a practical development environment, the completion of a job often necessitates the coordinated allocation of multiple resources. However, resource constraints such as molds are seldom taken into account in the multi-resource job shop scheduling problem. These molds have sequence-dependent setup times and are fully occupied during processing. Therefore, this study focuses on the Flexible Job Shop Scheduling Problem with Mold Resource Setup Time Optimization (FJSP-MRST), which aims to minimize setup time and makespan. Considering the characteristics of mold setup time, this paper introduces a Mold-Machine Double Fusion Insertion Method to optimize the mold-machine usage order and enhance the mold changeover time calculation method. This innovative approach improves the overall scheduling process by efficiently managing the utilization of molds and machines. Furthermore, we propose a Mold-Operation Layer Loading Method to compress setup time, offering a novel strategy for reducing the setup time for mold resources. These two pioneering methods are integrated with an enhanced multi-objective differential evolution algorithm to obtain the optimal solution for FJSP-MRST. Experimental results demonstrate that the proposed methods can render the arrangement of machines and molds under the current process sequence more reasonable and effectively eliminate implicit setup time. INDEX TERMSFlexible job shop scheduling problem, Mold resource, Setup time, Differential evolution algorithm I. INTRODUCTION

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Section: Multi-objective Differential Evolutionary Algorithmmentioning

confidence: 99%

Flexible Job Shop Scheduling Method for Optimizing Mold Resource Setup Time

Cheng,

Xie,

Xin

et al. 2024

IEEE Access

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“…and Taghippour [27] respectively used genetic algorithm, simulated annealing algorithm and teaching-learning-based optimization algorithm to solve the scheduling problem and proved the superiority of genetic algorithm in solving the scheduling problem by enumeration method. Similarly, the solution method based on genetic algorithm has also been adopted in some literatures and has shown its superiority in solving the job shop scheduling problem and other combinatorial optimization problems [10,14,16,19,23,24,26,32].…”

Section: One Equipment Can Be Competent For a Variety Of Processingmentioning

confidence: 99%

A genetic algorithm-based approach for flexible job shop rescheduling problem with machine failure interference

Liang,

Zhong,

Zhang

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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Rescheduling is the guarantee to maintain the reliable operation of production system process. In production system, the original scheduling scheme cannot be carried out when machine breaks down. It is necessary to transfer the production tasks in the failure cycle and replan the production path to ensure that the production tasks are completed on time and maintain the stability of production system. To address this issue, in this paper, we studied the event-driven rescheduling policy in dynamic environment, and established the usage rules of right-shift rescheduling and complete rescheduling based on the type of interference events. And then, we proposed the rescheduling decision method based on genetic algorithm for solving flexible job shop scheduling problem with machine fault inter-ference. In addition, we extended the "mk" series of instances by introducing the machine fault interference information. The solution data show that the complete rescheduling method can respond effectively to the rescheduling of flexible job shop scheduling problem with machine failure interference.

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