A Modified Ant Colony Optimization algorithm for the Distributed Job shop Scheduling Problem

被引：40
|
作者：
Chaouch, Iman [1 ,2 ]
Driss, Olfa Belkahla [1 ,3 ]
Ghedira, Khaled [1 ,4 ]
机构：
[1] Univ Manouba, COSMOS Lab, Tunis, Tunisia
[2] Univ Manouba, Ecole Natl Sci Informat, Tunis, Tunisia
[3] Univ Manouba, Ecole Super Commerce, Tunis, Tunisia
[4] Univ Tunis, Inst Super Gest Tunis, Tunis, Tunisia
来源：
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS | 2017年 / 112卷
关键词：
Bio-inspired Algorithm; Job shop; Makespan; Multi-factory; Scheduling; Swarm Intelligence; GENETIC ALGORITHM; SYSTEM;
D O I：
10.1016/j.procs.2017.08.267
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories geographically distributed and with determining a good operation schedule of each factory. The objective is to minimize the global makespan over all the factories. This paper is a first step to deal with the DJSP using three versions of a bio-inspired algorithm, namely the Ant Colony Optimization (ACO) which are the Ant System (AS), the Ant Colony System (ACS) and a Modified Ant Colony Optimization (MACO) aiming to explore more search space and thus guarantee better resolution of the problem. Comprehensive experiments are conducted to evaluate the performance of the three algorithms and the results show that the MACO is effective for the problem and AS and ACS algorithms in resolving the DJSP. (C) 2017 The Authors. Published by Elsevier B.V.
引用
收藏
页码：296 / 305
页数：10
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