Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 39, Issue 8, Pages 6683-6691Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.11.074
Keywords
Active learning genetic algorithm; Integrated process planning and scheduling; Process planning; Scheduling
Categories
Funding
- National Natural Science Foundation of China (NSFC) [51005088, 50825503]
- National Basic Research Program of China (973 Program) [2011CB706804]
- National High-Tech Research and Development Program of China (863 Program) [2012AA040909]
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In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem. (C) 2011 Elsevier Ltd. All rights reserved.
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