Desert sparrow optimization algorithm for the bicriteria flow shop scheduling problem with sequence-independent setup time

Sharma, Meenakshi; Sharma, Manisha; Sharma, Sameer

doi:10.1007/s12351-021-00675-w

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…They designed an adaptive genetic algorithm to solve the problem. Sharma et al [24] developed a mixed integer programming model for solving the dual-criteria flow shop scheduling problem with setup times independent of the sequence, proposing an improved heuristic algorithm based on the desert sparrow optimization (DSO) algorithm. Belabid et al [25] explored the permutation flow shop problem with independent setup time, where setup time depends on machine type and job preparation method.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…As mentioned in the previous section, due to its importance, the permutation flow shop scheduling problem is one of the most active problems in the operation literature with hundreds of contributions in the last years, addressing different variants and constraints in the classical problems. Recent examples solving the permutation flow shop can be found in [30][31][32][33][34][35][36], and addressing different setups configurations in [37][38][39][40][41][42][43][44]. For comprehensive reviews of the problem under different constraints, we refer the interested reader to [1,[45][46][47][48][49].…”

Section: Problem Description and Backgroundmentioning

confidence: 99%

The Permutation Flow Shop Scheduling Problem with Human Resources: MILP Models, Decoding Procedures, NEH-Based Heuristics, and an Iterated Greedy Algorithm

et al. 2022

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In this paper, we address the permutation flow shop scheduling problem with sequence-dependent and non-anticipatory setup times. These setups are performed or supervised by multiple servers, which are renewable secondary resources (typically human resources). Despite the real applications of this kind of human supervision and the growing attention paid in the scheduling literature, we are not aware of any previous study on the problem under consideration. To cover this gap, we start theoretically addressing the problem by: proposing three mixed-integer linear programming models to find optimal solutions in the problem; and proposing different decoding procedures to code solutions in approximated procedures. After that, the best decoding procedure is used to propose a new mechanism that generates 896 different dispatching rules, combining different measures, indicators, and sorting criteria. All these dispatching rules are embedded in the traditional NEH algorithm. Finally, an iterated greedy algorithm is proposed to find near-optimal solutions. By doing so, we provide academics and practitioners with efficient methods that can be used to obtain exact solutions of the problem; applied to quickly schedule jobs and react under changes; used for initialisation or embedded in more advanced algorithms; and/or easily updated and implemented in real manufacturing scenarios.

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