A hybrid cGA applied to the MLCLSP with overtime

Authors:
Claudio F. M. Toledo;Marcio S. Arantes;Renato R. R. de Oliveira;Alexandre C. B. Delbem
Affiliations:
University of Sao Paulo, SP, Brazil;University of Sao Paulo, SP, Brazil;Federal University of Lavras Lavras, MG, Brazil;University of Sao Paulo, SP, Brazil
Venue:
ACM SIGAPP Applied Computing Review
Year:
2013

Citing 10
Cited 0

A Lagrangean-based heuristic for dynamic multilevel multiitem constrained lotsizing with setup times

Management Science
Multilevel Lot Sizing with Setup Times and Multiple Constrained Resources: Internally Rolling Schedules with Lot-Sizing Windows

Operations Research
Solving a multi-level capacitated lot sizing problem with multi-period setup carry-over via a fix-and-optimize heuristic

Computers and Operations Research
Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs

Information Sciences: an International Journal
The compact genetic algorithm

IEEE Transactions on Evolutionary Computation
A hybrid heuristic for the traveling salesman problem

IEEE Transactions on Evolutionary Computation
A family of compact genetic algorithms for intrinsic evolvable hardware

IEEE Transactions on Evolutionary Computation
Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization

IEEE Transactions on Evolutionary Computation
A hybrid multi-population genetic algorithm applied to solve the multi-level capacitated lot sizing problem with backlogging

Computers and Operations Research
A hybrid compact genetic algorithm applied to the multi-level capacitated lot sizing problem

Proceedings of the 28th Annual ACM Symposium on Applied Computing

Quantified Score

Hi-index	0.00

Visualization

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.