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

Abstract 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.

[1]  Sun Hur,et al.  Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain , 2002 .

[2]  Egon Balas,et al.  The Shifting Bottleneck Procedure for Job Shop Scheduling , 1988 .

[3]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[4]  Khaled Ghédira,et al.  A Survey of Optimization Techniques for Distributed Job Shop Scheduling Problems in Multi-factories , 2017, CSOC.

[5]  Jacek Blazewicz,et al.  The job shop scheduling problem: Conventional and new solution techniques , 1996 .

[6]  Andrew Y. C. Nee,et al.  Web-based Multi-functional Scheduling System for a Distributed Manufacturing Environment , 2002, Concurr. Eng. Res. Appl..

[7]  Mauro Dell'Amico,et al.  Applying tabu search to the job-shop scheduling problem , 1993, Ann. Oper. Res..

[8]  Mitsuo Gen,et al.  Process Planning and Scheduling in Distributed Manufacturing System Using Multiobjective Genetic Algorithm , 2010 .

[9]  Rong Chen,et al.  A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem , 2011, Int. J. Comput. Intell. Syst..

[10]  A. Tamilarasi,et al.  Hybridizing tabu search with ant colony optimization for solving job shop scheduling problems , 2009 .

[11]  Rubén Ruiz,et al.  The distributed permutation flowshop scheduling problem , 2010, Comput. Oper. Res..

[12]  Khaled Ghédira,et al.  Minimizing makespan in multi-factory flow shop problem using a chemical reaction metaheuristic , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[13]  A. Azab,et al.  Modeling and heuristics for scheduling of distributed job shops , 2014, Expert Syst. Appl..

[14]  Andrew Y. C. Nee,et al.  A modified genetic algorithm for distributed scheduling problems , 2003, J. Intell. Manuf..

[15]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[16]  Andrew Y. C. Nee,et al.  Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems , 2007, Comput. Ind. Eng..

[17]  Ahmed Azab,et al.  An improved model and novel simulated annealing for distributed job shop problems , 2015 .

[18]  John J. Grefenstette.  Proceedings of the First International Conference on Genetic Algorithms and their Applications, July 24-26, 1985, at the Carnegie-Mellon University, Pittsburgh, PA , 1988 .

[19]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[20]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .