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

Imen Chaouch | Zendy; Olfa Belkahla Driss | Zendy; Khaled Ghédira | Zendy

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

Author(s) -

Imen Chaouch,

Olfa Belkahla Driss,

Khaled Ghédira

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.08.267

Subject(s) - ant colony optimization algorithms , computer science , job shop scheduling , scheduling (production processes) , metaheuristic , ant colony , schedule , mathematical optimization , algorithm , mathematics , operating system

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.

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