Evaluating genetic algorithms with different population structures on a lot sizing and scheduling problem

Authors:
Claudio Fabiano Motta Toledo;Paulo Morelato França;Kalianne Almeida Rosa
Affiliations:
Universidade Federal de Lavras, Lavras, MG, Brazil;Universidade Estadual, Prudente, SP, Brazil;Universidade Federal de Lavras, Lavras, MG, Brazil
Venue:
Proceedings of the 2008 ACM symposium on Applied computing
Year:
2008

Citing 3
Cited 0

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Abstract

This paper studies the use of different population structures in a Genetic Algorithm (GA) applied to lot sizing and scheduling problems. The population approaches are divided into two types: single-population and multi-population. The first type has a non-structured single population. The multi-population type presents non-structured and structured populations organized in binary and ternary trees. Each population approach is tested on lot sizing and scheduling problems found in soft drink companies. These problems have two interdependent levels with decisions concerning raw material storage and soft drink bottling. The challenge is to simultaneously determine the lot sizing and scheduling of raw materials in tanks and products in lines. Computational results are reported allowing determining the better population structure for the set of problem instances evaluated.