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An active learning genetic algorithm for integrated process planning and scheduling

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 th... Full description

Journal Title: Expert Systems With Applications 15 June 2012, Vol.39(8), pp.6683-6691
Main Author: Li, Xinyu
Other Authors: Gao, Liang , Shao, Xinyu
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0957-4174 ; E-ISSN: 1873-6793 ; DOI: 10.1016/j.eswa.2011.11.074
Link: https://www.sciencedirect.com/science/article/pii/S0957417411016204
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recordid: elsevier_sdoi_10_1016_j_eswa_2011_11_074
title: An active learning genetic algorithm for integrated process planning and scheduling
format: Article
creator:
  • Li, Xinyu
  • Gao, Liang
  • Shao, Xinyu
subjects:
  • Active Learning Genetic Algorithm
  • Integrated Process Planning and Scheduling
  • Process Planning
  • Scheduling
  • Computer Science
ispartof: Expert Systems With Applications, 15 June 2012, Vol.39(8), pp.6683-6691
description: 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.
language: eng
source:
identifier: ISSN: 0957-4174 ; E-ISSN: 1873-6793 ; DOI: 10.1016/j.eswa.2011.11.074
fulltext: fulltext
issn:
  • 0957-4174
  • 09574174
  • 1873-6793
  • 18736793
url: Link


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subjectActive Learning Genetic Algorithm ; Integrated Process Planning and Scheduling ; Process Planning ; Scheduling ; Computer Science
descriptionIn 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.
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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.

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