期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 61, 期 3, 页码 993-1012出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2021.2022801
关键词
Dynamic sequencing; simulation; mathematical optimisation; industry 4; 0; walking workers; parallel variable neighbourhood search
This paper discusses the impact of a newly emerging mass-individualisation concept on manufacturing systems, particularly the sequencing problem in dynamic environments. The author uses a real-world case to propose a mathematical model and a hyper-heuristic method for solving the problem, and employs a simulation approach for optimization in dynamic environments. The results indicate that the simulation approach is highly effective in solving such problems in real-world cases.
A newly emerging mass-individualisation concept has attracted increasing attention in recent years. However, this concept increases the complexity of manufacturing systems within organisations. In such systems, one of the main challenges is the sequencing problem, especially in dynamic environments where unpredictable events demand new constraints. In this context, the ability to use real-time data to make efficient, quick decisions has become one of the main priorities of managers. In this paper, based on a real-world case from Fiat Powertrain Technologies, we define a dynamic mixed-model assembly line sequencing problem with walking workers. In this context, each worker is assigned to a product for all assembly operations and moves from one station to another. A mathematical model is proposed to minimise production time. Since the problem is NP-hard, a hyper-heuristic is also developed to solve the problem. Moreover, a simulation-optimisation model is developed using FlexSim software to solve a real-world problem in a dynamic environment. Comparison of the results illustrates the effectiveness of using the simulation approach to dynamically solve such problems, especially in real-world cases. Finally, a thorough description of managerial insights is provided to indicate the applicability of the proposed approach.
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