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
Enhanced Logic-based Benders decomposition methods for the distributed heterogeneous non-permutation flow shop scheduling problem
Release time:2025-09-16 Hits:
- Impact Factor:7.3
- Journal:International Journal of Production Research
- Abstract:The PDF of this paper is available for download via the shared link: https://www.tandfonline.com/eprint/QRNBMUEDI8XT3AGUWWXJ/full?target=10.1080/00207543.2025.2553820. This paper addresses the Distributed Heterogeneous Non-Permutation Flowshop Scheduling Problem (DHNPFSP), where jobs are assigned to factories organised as non-permutation flowshops. Unlike the Distributed Heterogeneous Permutation Flowshop Scheduling Problem (DHPFSP), DHNPFSP allows varying job sequences across machines, expanding the solution space and potentially improving makespan. However, optimising multiple sequences simultaneously introduces significant computational challenges. To solve small-sized DHNPFSP instances, we formulate a Manne-based Mixed Integer Linear Programming (MILP) model and a Constraint Programming (CP) model. Given the ����-hard nature of the problem, we also integrate advanced techniques such as strong subproblem relaxations, tightened lower bound, iterated greedy, CP, and problem structure property, resulting in five enhanced Logic-Based Benders Decomposition (LBBD) approaches. These methods decompose the problem into an assignment master problem and multiple scheduling subproblems, which are solved using MILP and CP. Computational experiments show that CP-based methods outperform other approaches for small-sized instances, while the enhanced LBBD methods excel for medium- and large-sized instances. Comparative analysis indicates that in distributed flow shop production environments, the non-permutation scheme significantly improves makespan compared to the permutation scheme. Sensitivity analysis further reveals that increasing the number of factories and machines substantially escalates complexity, while the number of jobs has a minimal impact.
- Co-author:景琳,熊宁馨,向成飞
- First Author:xiongfuli
- Indexed by:Journal paper
- Discipline:Management Science
- First-Level Discipline:Management Science and Engineering
- Document Type:J
- Translation or Not:no
- Date of Publication:2025-01-01
- Included Journals:SCI
- Links to published journals:https://doi.org/10.1080/00207543.2025.2553820