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
Other Contact Information
- Email：
- Paper Publications
Meta-heuristics for the distributed two-stage assembly scheduling problem with bi-criteria of makespan and mean completion time
Release time:2024-08-09 Hits:
- Affiliation of Author(s):信息与控制工程学院
- Journal:International Journal of Production Research
- Key Words:中文关键字：分布式两阶段装配流水车间；调度；最大完工时间；平均完工时间，英文关键字：distributed two-stage assembly flowshop； schedulin
- Abstract:This paper focuses on the distributed two-stage assembly flowshop scheduling problem for minimising a weighted sum of makespan and mean completion time. This problem involves two inter-dependent decision sub-problems: (1) how to allocate jobs among factories and (2) how to schedule the assigned jobs at each factory. A mathematical model is formulated for solving the small-sized instances of the problem. Since the NP-hardness of the problem, we also proposed a variable neighbourhood search (VNS) algorithm and a hybrid genetic algorithm combined with reduced variable neighbourhood search (GA-RVNS) to solve the distributed two-stage assembly flowshop scheduling problems and approximately optimise makespan and mean completion time simultaneously. Computational experiments have been conducted to compare the performances of the model and proposed algorithms. For a set of small-sized instances, both the model and the proposed algorithms are effective. The proposed algorithms are further evaluated on a set of large-sized instances. The results statistically show that both GA-RVNS and VNS obtain much better performances than the GA without RVNS-based local search step (GA-NOV). For the instances with small numbers of jobs, VNS achieves better performances than GA-RVNS. However, for the instances with large numbers of jobs, GA-RVNS yields better performances than the VNS. It is also shown that the overall performances of VNS are very close to GA-RVNS with different numbers of factories, weights given to makespan and numbers of machines at the first stage.
- Note:熊福力
- First Author:xiongfuli
- Indexed by:Journal paper
- Correspondence Author:邢科义
- Volume:卷：52
- Issue:期：9
- Page Number:页：2743-2766
- Translation or Not:no
- Date of Publication:2014-05-01