A Hybrid cGA Applied to the MLCLSP with Overtime

Author(s)

Toledo, Claudio F. M. ; Arantes, Marcio S. ; de Oliveira, Renato R. R. ; Delbem, Alexandre C. B.

• Document type: Journal article
• Source: APPLIED COMPUTING REVIEW; v. 13, n. 3, p. 10-pg., 2013-09-01.

Abstract

A hybrid version of a compact genetic algorithm (cGA) is presented as approach to solve the Multi-Level Capacitated Lot Sizing Problem. The present paper extends results reported in [18]. The hybrid method combines a fix and optimize heuristic with cGA aiming to improve solutions generated by cGA. Also a linear mathematical programming model is solved to first evaluated solution provided by cGA. The performance of the hybrid compact genetic algorithm (HcGA) is evaluated over two sets of benchmark instances. The results are compared against methods from literature recently proposed for the same problem: two time-oriented decomposition heuristics and a hybrid multi-population genetic algorithm. A superior performance of HcGA is reported mainly for instances dealing with setup times and against time-oriented decomposition heuristics.(1) (AU)

• FAPESP's process: 11/15534-5 - Hybrid heuristics and metaheuristics applied to the multi-level capacitated lot sizing problem
• Grantee: Claudio Fabiano Motta Toledo
• Support Opportunities: Regular Research Grants
• FAPESP's process: 11/15581-3 - Environment for development of methods applied to optimization problems
• Grantee: Márcio da Silva Arantes
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 10/10133-0 - Cutting, packing, lot-sizing and scheduling problems and their integration in industrial and logistics settings
• Grantee: Reinaldo Morabito Neto
• Support Opportunities: Research Projects - Thematic Grants