Infeasibility handling in genetic algorithm using nested domains for production planning

• Type: Article in International Scientific Journal
• Date: 2010

Authors

Maristela Oliveira Santos (Author) FADEUP; Sadao Massago (Author) FADEUP; Bernardo Almada-Lobo (Author) FEUP

Journal

Title: Computers and Operations Research

Pages: 1113-1122

ISSN: 0305-0548

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 7 Citations

INSPEC

COMPENDEX

Other information

Authenticus ID: P-003-5XN

DOI: 10.1016/j.cor.2009.09.020

Abstract (EN): In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers. (C) 2009 Elsevier Ltd. All rights reserved.

Language: English

Type (Professor's evaluation): Scientific