An Improved Ant Colony Optimization for Flexible Job Shop Scheduling Problems

被引：24
|
作者：
Yao, Baozhen [2 ]
Yang, Chengyong [2 ]
Hu, Juanjuan [3 ,4 ]
Yao, Jinbao [2 ]
Sun, Jian [1 ]
机构：
[1] Tongji Univ, Sch Transportat Engn, Shanghai 200092, Peoples R China
[2] Beijing Jiaotong Univ, Sch Civil Engn, Beijing 100044, Peoples R China
[3] Minist Transport Peoples Republ China, Transport Management Inst, Beijing 101601, Peoples R China
[4] Beijing Univ Technol, Sch Civil Engn, Beijing 100022, Peoples R China
来源：
ADVANCED SCIENCE LETTERS | 2011年 / 4卷 / 6-7期
基金：
中国国家自然科学基金;
关键词：
Flexible Job Shop Scheduling Problem; Ant Colony Algorithm; Adaptive Parameters; Crossover Operation; VEHICLE-ROUTING PROBLEM; ALGORITHM; SEARCH;
D O I：
10.1166/asl.2011.1601
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Flexible job shop problem (FJSP), known as a well-known combinatorial optimization problem, is very important in fields of production management. An improved ant colony optimization (IACO) is presented to solve the FJSP in this paper. In IACO, three improved strategies, transition rule with adaptive parameters, crossover operation and pheromone updating strategy with adaptive parameters are introduced to enhance the performance of ACO. The effectiveness of IACO is examined by some best-known instances from literatures. Results show the proposed algorithm seems to be a powerful tool for FJSP compared with other heuristic algorithms.
引用
收藏
页码：2127 / 2131
页数：5
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