影响因子：

9.9

DOI码：

10.1016/j.aei.2025.103543

发表刊物：

Advanced Engineering Informatics

摘要：

This paper addresses a distributed heterogeneous flowshop scheduling problem (DHFSP) encountered in the prefabricated component (PC) manufacturing industry. Unlike traditional DHFSPs, the problem considered here involves factories equipped with sets of identical parallel flow lines that share resources. A distinctive feature of this problem is that processing times for the same stage may vary across different factories. The objective is to determine the optimal assignment of jobs to factories, the allocation of jobs to flow lines, and the sequencing of jobs processed by shared resources, with the aim of minimizing the makespan. To solve smaller instances of this problem, we first develop two models: a Manne-based mixed-integer linear programming (MILP) model and a constraint programming (CP) model. For larger instances, due to the problem’s inherent complexity, we propose an enhanced logic-based Benders decomposition approach (UL_LBBD_SSR). This method exploits the decomposable structure of the problem to efficiently obtain near-optimal solutions. UL_LBBD_SSR integrates the strengths of CP, MILP, problem structure-based lower bounds, and scheduling subproblem relaxations (SSRs). Experimental results demonstrate that UL_LBBD_SSR outperforms all other methods, solving all small-scale instances optimally and achieving an average optimality gap of 0.86% for large-scale instances. Furthermore, the effectiveness of key components, including the upper and lower bounds and SSRs, is thoroughly validated through extensive testing. This work introduces a novel decomposition-based approach for solving complex scheduling problems in PC manufacturing, offering practical insights for optimizing production efficiency in distributed, heterogeneous environments.

合写作者：

周楷昊,平安,景琳

第一作者：

熊福力

论文类型：

期刊论文

学科门类：

工学

一级学科：

计算机科学与技术

文献类型：

J

卷号：

67

页面范围：

103543

是否译文：

否

发表时间：

2025-01-01

收录刊物：

SCI

发布期刊链接：

https://doi.org/10.1016/j.aei.2025.103543