Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12857
Title: Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment : a case study based on the apparel industry
Authors: Guo, ZX
Wong, WK 
Leung, SYS 
Fan, JT
Chan, SF
Keywords: Apparel industry
Genetic algorithm
Job shop scheduling
Mathematical model
Optimization
Issue Date: 2006
Source: Computers and industrial engineering, 2006, v. 50, no. 3, p. 202-219 How to cite?
Journal: Computers and Industrial Engineering 
Abstract: An effective job shop scheduling (JSS) in the manufacturing industry is helpful to meet the production demand and reduce the production cost, and to improve the ability to compete in the ever increasing volatile market demanding multiple products. In this paper, a universal mathematical model of the JSS problem for apparel assembly process is constructed. The objective of this model is to minimize the total penalties of earliness and tardiness by deciding when to start each order's production and how to assign the operations to machines (operators). A genetic optimization process is then presented to solve this model, in which a new chromosome representation, a heuristic initialization process and modified crossover and mutation operators are proposed. Three experiments using industrial data are illustrated to evaluate the performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the JSS problem in a mixed- and multi-product assembly environment.
URI: http://hdl.handle.net/10397/12857
ISSN: 0360-8352
DOI: 10.1016/j.cie.2006.03.003
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