Hybrid Metaheuristic for Optimization Job-Shop Scheduling Problem

Year 2018, Volume: 1 Issue: 1, 1 - 9, 20.12.2018

Benhamza Karima , Zedadra Ouarda

Abstract

Real Job-shop scheduling problem is one of the most difficult NP-Combinatorial issues. Exact resolution methods cannot handle large size cases. It is therefore necessary to use heuristic methods to solve them within a reasonable time. There are a large number of metaheuristic, which have the advantage of covering only part of the search space to find an acceptable solution. In this work, Genetic Algorithm and Simulated Annealing are used to solve Job-shop scheduling problem. The objective is to find the sequence of operations on the machines that will minimize the total time required to complete the set of jobs, also known as the "Makespan". Compared to traditional genetic algorithm, hybrid approach yields significant improvement in solution quality.

Keywords

Scheduling Problem, Hybrid Metaheuristic, Optimization

References

• C alis, B., Bulkan, S.: A research survey: review of AI solution strategies of job shop scheduling problem. Journal of Intelligent Manufacturing, 26(5), 961-973 (2015).
• Boussad, I., Lepagnot, J., Siarry, P.: A survey on optimization metaheuristics. Information Sciences, 237, 82-117,(2013)
• Van Laarhoven, P. J., Aarts, E. H.: Simulated annealing. In Simulated annealing: Theory and applications (pp. 7-15). Springer, Dordrecht (1987).
• Gao, L.,Peng,C.Y.,Zhou,C.,Li,P.G.: Solving exible job shop scheduling problem using general particle swarm optimization. In Proceedings of the 36th CIE Conference on Computers and Industrial Engineering, pp.3018{3027, (2006).
• Behnke, Dennis, and Martin Josef Geiger. "Test instances for the exible job shop scheduling problem with work centers." (2012).
• J. Beasley. OR-Library. http://people.brunel.ac.uk/ mastjjb/jeb/info. html.