Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11129
Title: A resource-constrained assembly job shop scheduling problem with Lot Streaming technique
Authors: Wong, TC
Chan, FTS 
Chan, LY
Keywords: Assembly job shop
Genetic algorithm
Lot Streaming
Particle swarm optimization
Resource constraint
Issue Date: 2009
Publisher: Pergamon Press
Source: Computers and industrial engineering, 2009, v. 57, no. 3, p. 983-995 How to cite?
Journal: Computers and industrial engineering 
Abstract: To ensure effective shop floor production, it is vital to consider the capital investment. Among most of the operational costs, resource must be one of the critical cost components. Since each operation consumes resources, the determination of resource level is surely a strategic decision. For the first time, the application of Lot Streaming (LS) technique is extended to a Resource-Constrained Assembly Job Shop Scheduling Problem (RC_AJSSP). In general, AJSSP first starts with Job Shop Scheduling Problem (JSSP) and then appends an assembly stage for final product assembly. The primary objective of the model is the minimization of total lateness cost of all final products. To enhance the model usefulness, two more experimental factors are introduced as common part ratio and workload index. Hence, an innovative approach with Genetic Algorithm (GA) is proposed. To examine its goodness, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that GA can outperform PSO in terms of optimization power and computational effort for all test problems.
URI: http://hdl.handle.net/10397/11129
ISSN: 0360-8352
EISSN: 1879-0550
DOI: 10.1016/j.cie.2009.04.002
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