A genetic-simulated annealing algorithm for stochastic seru scheduling problem with deterioration and learning effect. (3rd April 2023)

• Record Type: Journal Article
• Authors: Zhang, Zhe; Shen, Ling; Gong, Xue; Zhong, Xiaofang; Yin, Yong
• Is Part Of: Journal of industrial and production engineering. Volume 40:Number 3(2023)
• Page Start: 205
• Page End: 222
• Publication Date: 2023-04-03
• Subjects: Seru scheduling -- genetic-simulated annealing algorithm -- learning effect -- resource allocation -- stochastic processing time; Industrial engineering -- Periodicals; Production engineering -- Periodicals; 620
• DOI: 10.1080/21681015.2023.2167875
• Languages: English
• ISSNs: 2168-1015

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: uf0001