Ant colony optimization for solving combinatorial fuzzy Job Shop Scheduling Problems

Author

Surekha, P. ; Mohanaraajan, P.R.A. ; Sumathi, S.

Author_Institution

PSG Coll. of Technol., Coimbatore, India

fYear

2010

fDate

27-29 Dec. 2010

Firstpage

295

Lastpage

300

Abstract

In this paper, we present an ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, which is also used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing the sequence of jobs. The sequence of jobs is scheduled using Fuzzy logic and optimized using ACO. The makespan, completion time, makespan efficiency, algorithmic efficiency and the elapsed time for the ant colony algorithm are evaluated. Computational results of the optimization algorithm are evaluated by analyzing the two popular JSSP benchmark instances, FT10 and the ABZ10 problems and the simulation is carried out using the software, MATLAB.

Keywords

combinatorial mathematics; fuzzy logic; fuzzy set theory; job shop scheduling; optimisation; ABZ10 problems; ACO; FT10 problems; JSSP benchmark instances; ant colony optimization; combinatorial fuzzy job shop scheduling problem; combinatorial optimization problem; fuzzy logic; job sequences; Ant colony optimization; Benchmark testing; Job shop scheduling; Optimization; Planning; Processor scheduling; Ant Colony Optimization; Fuzzy Logic; Job Shop Scheduling Problem; Makespan; Planning; Scheduling;

fLanguage

English

Publisher

ieee

Conference_Titel

Communication and Computational Intelligence (INCOCCI), 2010 International Conference on

Conference_Location

Erode

Type

conf

Filename

5738747