An Improved Evolutionary Algorithm to Sequence Operations on an ASRS Warehouse
This paper describes the hybridization of an evolutionary algorithm with a greedy algorithm to solve a job-shop problem with recirculation. We model a real problem that arises within the domain of loads’ dispatch inside an automatic warehouse. The evolutionary algorithm is based on random key representation. It is very easy to implement and allows the use of conventional genetic operators for combinatorial optimization problems. A greedy algorithm is used to generate active schedules. This constructive algorithm reads the chromosome and decides which operation is scheduled next. This option increases the efficiency of the evolutionary algorithm. The algorithm was tested using some instances of the real problem and computational results are presented.
KeywordsEvolutionary algorithms Job shop scheduling ASRS warehouse
This work was funded by the “Programa Operacional Fatores de Competitividade—COMPETE” and by the FCT—Fundação para a Ciência e Tecnologia in the scope of the project: FCOMP-01-0124-FEDER-022674.
- 1.Rashid, M.M., Kasemi, B., Rahman, M.: New Automated Storage and Retrieval System (ASRS) using wireless communications. In: 2011 4th International Conference on Mechatronics: Integrated Engineering for Industrial and Societal, Development, ICOM’11 (2011)Google Scholar
- 2.Crdenas, J.J., Garcia, A., Romeral, J.L., Andrade, F.: A genetic algorithm approach to optimization of power peaks in an automated warehouse. In: Proceedings of Industrial Electronics Conference (IECON), pp. 3297–3302 (2009)Google Scholar
- 4.Oliveira, J.A.: A genetic algorithm with a quasi-local search for the job shop problem with recirculation. In: Abraham, A., de Barts, B., Köppen, M., Nickolay, B. (eds.) Applied Soft Computing Technologies: The Challenge of Complexity, pp. 221–234. Springer, Heidelberg (2006)Google Scholar
- 12.Oliveira, J.A., Dias, L., Pereira, G.: Solving the job shop problem with a random keys genetic algorithm with instance parameters. In: Proceedings of 2nd International Conference on Engineering Optimization (EngOpt 2010), Lisbon, Portugal, (CDRom) (2010)Google Scholar