Minimizing makespan of stochastic customer orders in cellular manufacturing systems with parallel machines. (January 2021)
- Record Type:
- Journal Article
- Title:
- Minimizing makespan of stochastic customer orders in cellular manufacturing systems with parallel machines. (January 2021)
- Main Title:
- Minimizing makespan of stochastic customer orders in cellular manufacturing systems with parallel machines
- Authors:
- Wu, Lang
Zhao, Yaping
Feng, Yuanyue
Niu, Ben
Xu, Xiaoyun - Abstract:
- Highlights: Enrich cellular manufacturing literature by considering stochastic customer orders. Extend customer order production problems to stochastic multi-stage processing. Optimality properties are explored along with several important special cases. Easily implementable algorithms are constructed and shown to be effective and robust. Abstract: This study addresses a stochastic customer order production problem in cellular manufacturing systems with parallel machines. The objective is to minimize the long-run expected makespan of stochastic customer orders via proper cellular manufacturing system designs under a budget limit. Theoretical studies are conducted to explore the effect of demand uncertainty and production requirement on the optimal design and the objective. Explicit expressions are provided for dterministic workload case and several production requirement cases, which results in insights into basic principles for the optimal design to possess. Based on the theoretical analysis, three heuristic algorithms are further established. Experimental results demonstrate the effectiveness of the proposed algorithms under a variety of production scenarios. This paper extends the existing literature on customer orders by involving in stochastic arrivals and demands whose multi-stage processing is conducted in manufacturing cells. Besides, studies on design of cellular manufacturing system are enriched with the consideration of stochastic customer orders that requireHighlights: Enrich cellular manufacturing literature by considering stochastic customer orders. Extend customer order production problems to stochastic multi-stage processing. Optimality properties are explored along with several important special cases. Easily implementable algorithms are constructed and shown to be effective and robust. Abstract: This study addresses a stochastic customer order production problem in cellular manufacturing systems with parallel machines. The objective is to minimize the long-run expected makespan of stochastic customer orders via proper cellular manufacturing system designs under a budget limit. Theoretical studies are conducted to explore the effect of demand uncertainty and production requirement on the optimal design and the objective. Explicit expressions are provided for dterministic workload case and several production requirement cases, which results in insights into basic principles for the optimal design to possess. Based on the theoretical analysis, three heuristic algorithms are further established. Experimental results demonstrate the effectiveness of the proposed algorithms under a variety of production scenarios. This paper extends the existing literature on customer orders by involving in stochastic arrivals and demands whose multi-stage processing is conducted in manufacturing cells. Besides, studies on design of cellular manufacturing system are enriched with the consideration of stochastic customer orders that require synchronized arrival and departure for products within an order. … (more)
- Is Part Of:
- Computers & operations research. Volume 125(2021)
- Journal:
- Computers & operations research
- Issue:
- Volume 125(2021)
- Issue Display:
- Volume 125, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 125
- Issue:
- 2021
- Issue Sort Value:
- 2021-0125-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-01
- Subjects:
- Stochastic customer order -- Makespan minimization -- Cellular manufacturing system -- Parallel machines
Operations research -- Periodicals
Electronic digital computers -- Periodicals
004.05 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03050548 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cor.2020.105101 ↗
- Languages:
- English
- ISSNs:
- 0305-0548
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.770000
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