Journal
JOURNAL OF INTELLIGENT MANUFACTURING
Volume 25, Issue 5, Pages 867-879Publisher
SPRINGER
DOI: 10.1007/s10845-013-0746-x
Keywords
Cooperative estimation of distribution algorithm; Manufacturing management; Flexible manufacturing systems; Semiconductor final test scheduling problems
Funding
- Japan Society of Promotion of Science [245102190001]
- National Science Council, Taiwan [NSC101-2811-E-007-004, NSC100-2410-H-031-011-MY2, NSC100-2628-E-007-017-MY3]
- Advanced Manufacturing and Service Management Research Center of National Tsing Hua University [101N2073E1]
- Grants-in-Aid for Scientific Research [24510219] Funding Source: KAKEN
Ask authors/readers for more resources
A 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available