A Survey of Recent Developments for JSSP and FJSSP Using ACO

Author:

Mehmood Nasir1,Umer Muhammad1,Riaz Ahmad1

Affiliation:

1. National University of Sciences and Technology

Abstract

Ant Colony Optimization (ACO) is based on swarm intelligence and it is a constructive meta-heuristic which was first presented in 1991. Job Shop Scheduling Problem (JSSP) is very important problem of the manufacturing industry. JSSP is a combinatorial optimization problem which is NP-hard. The exact solution of NP-hard problem is very difficult to find. Therefore heuristics approach is the best approach for such problems. This paper shall overview the application of ant colony optimization on JSSP and Flexible Job Shop Scheduling problems (FJSSP). This paper shalll cover the major areas in which researchers have worked and it shall also recommend the future area of research in the light of this overview. This paper will also cover the quantitative analysis of the research papers which are considered in this survey. Based upon this survey some conclusions are drawn in the end.The significance of this paper is that it has covered all the efforts and major researches in the area of ACO application on JSSP and FJSSP through the inception of ACO metaheuristics. This enables the researchers and scheduling experts to overview chronologically the development of ACO on JSSP and FJSSP.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference22 articles.

1. Alberto Colorni, Marco Dorigo, Vittorio Maniezzo and Marco Trubian Ant system for Job-shop Scheduling.

2. Sjoerd van der Zwaan and Carlos Marques. Ant Colony Optimisation for Job Shop Scheduling.

3. Mario Ventresca and Beatrice M. Ombuki. Ant Colony Optimization for Job Shop Scheduling Problem.

4. De-lin Luo, Shun-xiang Wu, Mao-qing Li and Zhong Yang. Ant Colony Optimization with Local Search Applied to the Flexible Job Shop Scheduling Problems. 978-1-4244-2064-3/08/$25. 00 ©2008 IEEE.

5. Kuo-Ling Huang and Ching-Jong Liao. Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers & Operations Research 35 (2008) 1030 – 1046.

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