Parallel-serial batch no-wait flowshop scheduling for makespan minimization

This paper investigates a parallel-serial batch no-wait flowshop scheduling problem with the objective of minimising the makespan. The system consists of a parallel-batch machine at the first stage, where the batch processing time is determined by the longest job, and a serial-batch machine at the second stage, where the batch processing time is equal to the sum of job processing times within this batch plus a constant setup time. The batch capacity is limited, and all jobs have identical processing times on the second machine. We propose a mixed-integer linear programming model and establish a series of structural properties that precisely characterise the form of optimal schedules. Based on these properties, we propose a dynamic programming algorithm that computes optimal solutions in polynomial time O(n4). Computational experiments demonstrate that, compared with the existing algorithm, the proposed algorithm consistently finds optimal solutions, while being significantly more efficient than Gurobi.

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Publication Details

Journal
Journal of the Operational Research Society
Published
2026-11-03
DOI
https://doi.org/10.1080/01605682.2026.2750206
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
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article

Parallel-serial batch no-wait flowshop scheduling for makespan minimization

Yao‐Wen Sang, Naiming Xie, Ling-Hao Ji, Jian Chen
Journal of the Operational Research Society
Scheduling and Optimization Algorithms
article

Parallel-serial batch no-wait flowshop scheduling for makespan minimization

Yao‐Wen Sang, Naiming Xie, Ling-Hao Ji, Jian Chen
article en

Abstract

This paper investigates a parallel-serial batch no-wait flowshop scheduling problem with the objective of minimising the makespan. The system consists of a parallel-batch machine at the first stage, where the batch processing time is determined by the longest job, and a serial-batch machine at the second stage, where the batch processing time is equal to the sum of job processing times within this batch plus a constant setup time. The batch capacity is limited, and all jobs have identical processing times on the second machine. We propose a mixed-integer linear programming model and establish a series of structural properties that precisely characterise the form of optimal schedules. Based on these properties, we propose a dynamic programming algorithm that computes optimal solutions in polynomial time O(n4). Computational experiments demonstrate that, compared with the existing algorithm, the proposed algorithm consistently finds optimal solutions, while being significantly more efficient than Gurobi.

Journal of the Operational Research Society
Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 15%
Scheduling and Optimization Algorithms
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