xiongfuli
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- Associate Professor
- Supervisor of Master's Candidates
- Name (Pinyin):xiongfuli
- School/Department:信息与控制工程学院
- Education Level:With Certificate of Graduation for Doctorate Study
- Degree:Doctoral Degree in Engineering
- Professional Title:Associate Professor
- Status:Employed
- Academic Titles:西安建筑科技大学信息与控制工程学院副教授
- Alma Mater:东南大学
- Teacher College:信息与控制工程学院
- Discipline:Control Science and Engineering
Control Theory and Control Engineering
Systems Engineering

- Email:
- Paper Publications
Scheduling distributed heterogeneous parallel precast flowshop with shared resources via logic-based Benders decomposition
Release time:2025-06-20 Hits:
- Impact Factor:9.9
- DOI number:10.1016/j.aei.2025.103543
- Journal:Advanced Engineering Informatics
- Abstract: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.
- Co-author:周楷昊,平安,景琳
- First Author:熊福力
- Indexed by:Journal paper
- Discipline:Engineering
- First-Level Discipline:Computer Science and Technology
- Document Type:J
- Volume:67
- Page Number:103543
- Translation or Not:no
- Date of Publication:2025-01-01
- Included Journals:SCI
- Links to published journals:https://doi.org/10.1016/j.aei.2025.103543