A genetic-simulated annealing algorithm for stochastic seru scheduling problem with deterioration and learning effect

IF 4 Q2 ENGINEERING, INDUSTRIAL

Journal of Industrial and Production Engineering Pub Date : 2023-02-10 DOI:10.1080/21681015.2023.2167875

Zhe Zhang, Ling Shen, Xue Gong, X. Zhong, Yong Yin

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引用次数: 1

Abstract

ABSTRACT As a flexible and effective production mode, seru production has been adopted successfully in electronic industry. In practice, the processing time may be affected by many stochastic factors, such as worker absence, shortage of resources, and so on. This paper focuses on seru scheduling problems with stochastic processing time, and considers the influence of dynamic resource allocation, job deterioration, learning effecteffect, and setup time simultaneously to minimize the makespan. A genetic-simulated annealing algorithm is proposed, in which a simulated annealing procedure is constructed to re-optimize the optimal individual obtained by geneticthe genetic algorithm. Experiment results validate the effectiveness of proposedthe proposed seru scheduling model and genetic-simulated annealing algorithm for solving large-scale cases, and indicate that the stochastic processing time has a great influence on the makespan whichmakespan that can help production manager to makeproduce more consistent results according to the actual situation. Graphical abstract

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具有退化和学习效应的随机seru调度问题的遗传模拟退火算法

丝质生产作为一种灵活有效的生产方式，在电子工业中得到了成功的应用。在实际操作中，加工时间可能受到许多随机因素的影响，如工人缺勤、资源短缺等。研究了具有随机加工时间的批量调度问题，同时考虑了动态资源分配、作业劣化、学习效应和设置时间的影响，使最大完工时间最小化。提出了一种遗传模拟退火算法，该算法通过构造模拟退火程序对遗传算法得到的最优个体进行再优化。实验结果验证了所提出的序列调度模型和遗传模拟退火算法在解决大规模案例中的有效性，并表明随机处理时间对最大作业时间的影响很大，最大作业时间可以帮助生产管理者根据实际情况产生更一致的结果。图形抽象

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来源期刊

Journal of Industrial and Production Engineering ENGINEERING, INDUSTRIAL-

CiteScore

7.50

自引率

6.70%

发文量