Multirecombinated Evolutionary Algorithms for the Flow Shop Scheduling Problem

Authors:
Susana C. Esquivel;Federico Zuppa;Raúl Héctor Gallard
Affiliations:
-;-;-
Venue:
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Year:
2000

Citing 3
Cited 0

A genetic algorithm for flowshop sequencing

Computers and Operations Research - Special issue on genetic algorithms
A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization

Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.

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Abstract

Over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling Problem (FSSP) by means of well-known heuristics which, are successfully used for certain instances of the problem providing a single acceptable solution. Current trends involve distinct evolutionary computation approaches. This work shows an implementation of diverse evolutionary approaches on a set of flow shop scheduling instances, including latest approaches using a multirecombination feature. Multiple Crossovers per Couple (MCPC), and partial replacement of the population when possible stagnation is detected. A discussion on implementation details, analysis and a comparison of evolutionary and conventional approaches to the problem are shown.