Resource-constrained multi-stage processing and assembly scheduling with sequential and batch operations

Song Wu, Yang Wang, Yu Du, Wei Yang and Jianguang Feng ()
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Song Wu: Northwestern Polytechnical University, School of Management
Yang Wang: Northwestern Polytechnical University, School of Management
Yu Du: University of Colorado Denver, Business School
Wei Yang: Northwestern Polytechnical University, School of Management
Jianguang Feng: Northwestern Polytechnical University, School of Management

Journal of Heuristics, 2025, vol. 31, issue 4, No 7, 41 pages

Abstract: Abstract This paper addresses a complex scheduling problem in multi-product, flexible, and small-batch production modes, where the aim is to meet diverse customer demands while enhancing production efficiency. Specifically, the problem involves fulfilling a set of orders, each containing multiple products. Each product consists of several components that must undergo an assembly operation after being manufactured through a series of sequential or batch operations. These operations require various resources, such as machines, personnel, and equipment, in both the processing and assembly stages. To solve this complex problem, we develop a discrete-time mixed integer programming model that incorporates multi-stage processing and assembly, as well as sequential and batch processing, subject to resource constraints. For large-scale problems, we propose a simple and effective heuristic in which the initial solution is constructed using a parallel schedule generation scheme based on the minimum latest finish time priority rule. This solution is then iteratively refined through alternating forward and backward scheduling procedures. The proposed heuristic is evaluated on both small and large problem instances with various resource capacities and order quantities. Numerical experiments demonstrate that the proposed heuristic outperforms the multi-start parallel schedule generation scheme for all 120 instances, with improvements of 2.22%-5.93%. In addition, the comparison results under standard resource capacity indicate that it also outperforms the random sampling method and two classic metaheuristic algorithms for all 40 instances, achieving 3.90%-26.05% improvements. Finally, resource utilization analysis offers valuable insights into the efficiency of the scheduling outcomes.

Keywords: Resource constraints; Multi-stage scheduling; Sequential and batch processing; Heuristic; Forward-backward improvement (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10732-025-09573-2

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