Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32111
Title: A genetic algorithm approach to the multiple machine tool selection problem
Authors: Keung, KW
Ip, WH 
Lee, TC
Keywords: Flexible machining workstation (FMW)
Genetic algorithms (GA)
Job scheduling problem
Machine assignment problem
Tool sharing
Tool switching
Tool switching instances
Issue Date: 2001
Publisher: Kluwer Academic Publ
Source: Journal of intelligent manufacturing, 2001, v. 12, no. 4, p. 331-342 How to cite?
Journal: Journal of Intelligent Manufacturing 
Abstract: A number of earlier researches have emphasized the on-the-job scheduling problems that occur with a single flexible machine. Two solutions to the problem have generally been considered; namely minimization of tool switches and minimization of tool switching instances. Methods used to solve the problems have included KTNS heuristic, dual-based relaxation heuristic, and non-LP-based branch-and-bound methods. However, scant literature has considered the case of job scheduling on multiple parallel machines which invokes another problem involving machine assignment. This paper addresses the problem of job scheduling and machine assignment on a flexible machining workstation (FMW) equipped with multiple parallel machines in a tool-sharing environment. Under these circumstances, the authors have attempted to model the problem with the objective of simultaneously minimizing both the number of tool switches and the number of tool switching instances. Furthermore, a set of realistic constraints has been included in the investigation. A novel genetic algorithm (GA) heuristic has been developed to solve the problem, and performance results show that GA is an appropriate solution.
URI: http://hdl.handle.net/10397/32111
ISSN: 0956-5515
DOI: 10.1023/A:1011215416734
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