Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207540110119991
Title: Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts
Authors: Li, W.D.
Ong, S.K. 
Nee, A.Y.C. 
Issue Date: 20-May-2002
Source: Li, W.D.,Ong, S.K.,Nee, A.Y.C. (2002-05-20). Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. International Journal of Production Research 40 (8) : 1899-1922. ScholarBank@NUS Repository. https://doi.org/10.1080/00207540110119991
Abstract: For a CAPP system in a dynamic workshop environment, the activities of selecting machining resources, determining set-up plans and sequencing machining operations should be considered simultaneously to achieve the global lowest machining cost. Optimizing process plans for a prismatic part usually suffer from complex technological requirements and geometric relationships between features in the part. Here, process planning is modelled as a combinatorial optimization problem with constraints, and a hybrid genetic algorithm (GA) and simulated annealing (SA) approach has been developed to solve it. The evaluation criterion of machining cost comes from the combined strengths of machine costs, cutting tool costs, machine changes, tool changes and set-ups. The GA is carried out in the first stage to generate some initially good process plans. Based on a few selective plans with Hamming distances between each other, the SA algorithm is employed to search for alternative optimal or near-optimal process plans. In the GA and SA algorithms, some preliminarily defined precedence constraints between features and operations are manipulated. A case study and the comparisons with the single GA and SA approaches show that this hybrid approach can achieve highly satisfactory results.
Source Title: International Journal of Production Research
URI: http://scholarbank.nus.edu.sg/handle/10635/60463
ISSN: 00207543
DOI: 10.1080/00207540110119991
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