Solving Fuzzy Job-Shop Scheduling Problem by a Hybrid PSO Algorithm

被引：0
|
作者：
Li, Junqing [1 ]
Pan, Quan-Ke [1 ]
Suganthan, P. N. [2 ]
Tasgetiren, M. Fatih [3 ]
机构：
[1] Liaocheng Univ, Sch Comp Sci, Liaocheng 252059, Shandong, Peoples R China
[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
[3] Yasar Univ, Dept Ind Engn, Izmir, Turkey
来源：
SWARM AND EVOLUTIONARY COMPUTATION | 2012年 / 7269卷
基金：
美国国家科学基金会;
关键词：
Fuzzy processing time; Job-shop scheduling problem; Particle swarm optimization; Tabu search; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; PROCESSING TIME; TABU SEARCH;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper proposes a hybrid particle swarm optimization (PSO) algorithm for solving the job-shop scheduling problem with fuzzy processing times. The objective is to minimize the maximum fuzzy completion time, i.e., the fuzzy makespan. In the proposed PSO-based algorithm performs global explorative search, while the tabu search (TS) conducts the local exploitative search. One-point crossover operator is developed for the individual to learn information from the other individuals. Experimental results on three well-known benchmarks and a randomly generated case verify the effectiveness and efficiency of the proposed algorithm.
引用
收藏
页码：275 / 282
页数：8
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