A hybrid fluid master–apprentice evolutionary algorithm for large-scale multiplicity flexible job-shop scheduling with sequence-dependent set-up time

Ding, Linshan; Guan, Zailin; Zhang, Zhengmin; Fang, Weikang; Chen, Zhipeng; Yue, Lei

doi:10.1080/0305215x.2022.2145605

Cited by 1 publication

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“…The literature contains extensive research on the DFJSP, but few studies have addressed the MDFJSP [18][19][20]. Ding et al (2022) [21] proposed a fluid masterapprentice evolutionary method to address the multiplicity flexible job shop scheduling problem, and Ding et al (2024) [22] presented a multi-policy deep reinforcement learning method for multi-objective multiplicity flexible job shop scheduling problem. But these two papers are only applicable to static scheduling problems, and are not suitable for addressing dynamic scheduling challenges.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Search Algorithm for Multiplicity Dynamic Flexible Job Shop Scheduling with New Order Arrivals

Ding,

Guan,

Luo

et al. 2024

Symmetry

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In today’s customer-centric economy, the demand for personalized products has compelled corporations to develop manufacturing processes that are more flexible, efficient, and cost-effective. Flexible job shops offer organizations the agility and cost-efficiency that traditional manufacturing processes lack. However, the dynamics of modern manufacturing, including machine breakdown and new order arrivals, introduce unpredictability and complexity. This study investigates the multiplicity dynamic flexible job shop scheduling problem (MDFJSP) with new order arrivals. To address this problem, we incorporate the fluid model to propose a fluid randomized adaptive search (FRAS) algorithm, comprising a construction phase and a local search phase. Firstly, in the construction phase, a fluid construction heuristic with an online fluid dynamic tracking policy generates high-quality initial solutions. Secondly, in the local search phase, we employ an improved tabu search procedure to enhance search efficiency in the solution space, incorporating symmetry considerations. The results of the numerical experiments demonstrate the superior effectiveness of the FRAS algorithm in solving the MDFJSP when compared to other algorithms. Specifically, the proposed algorithm demonstrates a superior quality of solution relative to existing algorithms, with an average improvement of 29.90%; and exhibits an acceleration in solution speed, with an average increase of 1.95%.

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Section: Introductionmentioning

confidence: 99%

“…Based on Equation (21), where m ′ with the maximum value of gap mr ′ j ′ (t) is chosen to perform operation o r ′ n ′ j ′ .…”

mentioning

confidence: 99%

An Adaptive Search Algorithm for Multiplicity Dynamic Flexible Job Shop Scheduling with New Order Arrivals

Ding,

Guan,

Luo

et al. 2024

Symmetry

Self Cite

View full text Add to dashboard Cite

show abstract

A hybrid fluid master–apprentice evolutionary algorithm for large-scale multiplicity flexible job-shop scheduling with sequence-dependent set-up time

Cited by 1 publication

References 39 publications

An Adaptive Search Algorithm for Multiplicity Dynamic Flexible Job Shop Scheduling with New Order Arrivals

An Adaptive Search Algorithm for Multiplicity Dynamic Flexible Job Shop Scheduling with New Order Arrivals

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