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4. Scheduling a tempered glass manufacturing system: a three-stage hybrid flow shop model

Author

Yuan, Zhe, Xu, Haoxuan, Gong, Yeming (Yale), Chu, Chengbin, Zhang, Jinlong, Zou, Bipan, Xu, Xianhao, Liu, Ming, Yang, Xuenan, Zhang, Jiantong, CentraleSupélec, Zhongnan University of Economics and Law [China], emlyon business school, Laboratoire Génie Industriel - EA 2606 (LGI), Institut de recherches sur la catalyse et l'environnement de Lyon (IRCELYON), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), School of Economics and Management, and Tongji University

Subjects
0209 industrial biotechnology, Mathematical optimization, Engineering, 021103 operations research, Three stage, Job shop scheduling, business.industry, Strategy and Management, 0211 other engineering and technologies, Scheduling (production processes), Toughened glass, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 02 engineering and technology, Flow shop scheduling, Management Science and Operations Research, Manufacturing systems, GeneralLiterature_MISCELLANEOUS, Industrial and Manufacturing Engineering, 020901 industrial engineering & automation, Simulated annealing, Batch processing, business, ComputingMilieux_MISCELLANEOUS
Abstract

Chinese tempered glass has entered a fast and stable growing era. To improve the productivity of tempered glass manufacturers, this paper investigates a scheduling problem in tempered glass production system, originated from a tempered glass manufacturer in China. This problem can be formulated as a three-stage hybrid flow shop (HFS). Single and batch processing machines coexist in this HFS. Besides, a limited buffer, between the first two stages, and machine eligibility requirement are also significant characteristics. To address this complicated scheduling problem, we first establish an integer programming model with the objective of minimising the makespan, i.e. the maximum completion time of jobs in the system. Due to the strong NP-hard nature of the problem, we then propose a constructive heuristic method, a genetic algorithm, as well as a simulated annealing algorithm, to solve practical large-scale problems. Computational results demonstrate the efficiency of the proposed approaches.

Published
2017

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