Business Faculty Publications
Document Type
Article
Publication Date
2009
Publication Title
Computers & Operations Research
Keywords
Computer Science
Disciplines
Management Information Systems
Abstract
This paper uses a genetic algorithm to solve the order-acceptance problem with tardiness penalties. We compare the performance of a myopic heuristic and a genetic algorithm, both of which do job acceptance and sequencing, using an upper bound based on an assignment relaxation. We conduct a pilot study, in which we determine the best settings for diversity operators (clone removal, mutation, immigration, population size) in connection with different types of local search. Using a probabilistic local search provides results that are almost as good as exhaustive local search, with much shorter processing times. Our main computational study shows that the genetic algorithm always dominates the myopic heuristic in terms of objective function, at the cost of increased processing time. We expect that our results will provide insights for the future application of genetic algorithms to scheduling problems.
Recommended Citation
Rom, W. O., Slotnick, S. A. (2009). "Order Acceptance Using Genetic Algorithms". Computers & Operations Research, 36, pp. 1758-1767.
DOI
10.1016/j.cor.2008.04.010
Version
Postprint
Publisher's Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Operations Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Operations Research, 36 (2009), 10.1016/j.cor.2008.04.010
Volume
36
