Maximum completion time under a learning effect in the permutation flowshop scheduling problem
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Date
2018
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Univ Cincinnati Industrial Engineering
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
The permutation flowshop scheduling problem under a position-based learning effect is addressed in this study. Minimization of the maximum completion time (make span) is considered for the identified problem. The mathematical programming model is established to find optimal solutions for small-sized problems. Furthermore, meta-heuristics are developed to achieve effective solutions for large-sized problems encountered in real applications. These meta-heuristics are the genetic algorithm which is a population-based solution approach, the kangaroo and the variable neighborhood search algorithms which both are single-solution-based solution approaches. In addition, different solution methods, which are in the literature for similar problem structures, are also used. Improved heuristics are evaluated according to optimal results for small-sized problems and according to performance differences between each other for large-sized problems.
Description
Keywords
learning effect, flowshop, make span, genetic algorithm, kangaroo algorithm, variable neighborhood search algorithm
Journal or Series
International Journal Of Industrial Engineering-Theory Applications And Practice
WoS Q Value
Q4
Scopus Q Value
Q3
Volume
25
Issue
2
Citation
closedAccess
