Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems
- 1.8k Downloads
This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.
KeywordsFlexbile job shop scheduling problem Discrete artificial bee colony Multi-objective optimization Crossover operator
Unable to display preview. Download preview PDF.
- 3.Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res 22:158–183Google Scholar
- 6.Gao L, Peng CY, Zhou C, Li PG (2006) Solving flexible job shop scheduling problem using general particle swarm optimization. In: Proceedings of the 36th CIE Conference on Computers & Industrial Engineering, Taipei, China, June 20–23, 2006, pp. 3018–3027Google Scholar
- 7.Liouane N, Saad I, Hammadi S, Borne P (2007) Ant systems & local search optimization for flexible job-shop scheduling production. Int J Comput Commun Control 2:174–184Google Scholar
- 15.Kacem I, Hammadi S, Borne P (2002) Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE T Syst Man Cy C, Part C 32(1):408–419Google Scholar
- 25.Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Technical Report TR06. Computer Engineering Department, Erciyes University, TurkeyGoogle Scholar
- 33.Wang L (2003) Shop scheduling with genetic algorithms. Tsinghua university press, BeijingGoogle Scholar
- 34.Ho NB, Tay JC (2004) GENACE: an efficient cultural algorithm for solving the flexible job-shop problem. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC2004) Piscataway, pp. 1759–1766Google Scholar