Paper Info
Title
The cooperative estimation of distribution algorithm: a novel approach for semiconductor final test scheduling problems
Abstract
large number of studies have been conducted in the area of semiconductor final test scheduling (SFTS) problems. As a specific example of the simultaneous multiple resources scheduling problem, intelligent manufacturing planning and scheduling based on meta-heuristic methods, such as the genetic algorithm (GA), simulated annealing, and particle swarm optimization, have become common tools for finding satisfactory solutions within reasonable computational times in real settings. However, only a few studies have analyzed the effects of interdependent relations during group decision-making activities. Moreover, for complex and large problems, local constraints and objectives from each managerial entity and their contributions toward global objectives cannot be effectively represented in a single model. This paper proposes a novel cooperative estimation of distribution algorithm (CEDA) to overcome these challenges. The CEDA extends a co-evolutionary framework incorporating a divide-and-conquer strategy. Numerous experiments have been conducted, and the results confirmed that CEDA outperforms hybrid GAs for several SFTS problems.
Year
DOI
Venue
2014
10.1007/s10845-013-0746-x
Journal of Intelligent Manufacturing
Keywords
Field
DocType
Cooperative estimation of distribution algorithm,Manufacturing management,Flexible manufacturing systems,Semiconductor final test scheduling problems
Particle swarm optimization,Simulated annealing,Interdependence,Mathematical optimization,Job shop scheduling,Fair-share scheduling,Estimation of distribution algorithm,Scheduling (computing),Engineering,Genetic algorithm
Journal
Volume
Issue
ISSN
25
5
0956-5515
Citations 
PageRank 
References 
15
0.84
15
Authors
4
Name
Order
Citations
PageRank
Xin-Chang Hao1665.19
Jei-Zheng Wu2959.40
Chen-Fu Chien362358.23
Mitsuo Gen41873130.43