A genetic algorithm for the job shop on an ASRS warehouse

By Figueiredo, J.; Oliveira, J.A.; Dias, L.; Pereira, G.A.B.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2012

Abstract

This paper describes the application of a metaheuristic to a real problem that arises within the domain of loads’ dispatch inside an automatic warehouse. The truck load operations on an automated storage and retrieval system warehouse could be modeled as a job shop scheduling problem with recirculation. The genetic algorithm is based on random key representation, that is very easy to implement and it allows the use of conventional genetic operators for combinatorial optimization problems. This genetic algorithm includes specific knowledge of the problem to improve its efficiency. A constructive algorithm based in Giffler-Thompson’s algorithm is used to generate non delay plans. The constructive algorithm reads the chromosome and decides which operation is scheduled next. This option increases the efficiency of the genetic algorithm. The algorithm was tested using some instances of the real problem and computational results are presented.

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