Multi-objective genetic algorithm for integrated process planning and scheduling with fuzzy processing time

被引：0

作者：

Wen, Xiaoyu ^{[1
]}

Li, Xinyu ^{[1
]}

Gao, Liang ^{[1
]}

Wan, Liang ^{[1
]}

Wang, Wenwen ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China

来源：

2013 SIXTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | 2013年

关键词：

Integrated process planning and scheduling; multi-objective genetic algorithm; fuzzy processing time;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. In real manufacturing environment, multi-objectives should be taken into consideration simultaneously during the machining process. Meanwhile, the processing time for each job is often imprecise in many real applications. Therefore, multi-objective integrated process planning and scheduling (IPPS) problem with fuzzy processing time is addressed in this paper. The processing time is described as triangular fuzzy number. A multi-objective genetic algorithm (MOGA) is designed to search for the Pareto solutions of multiobjective IPPS problem with fuzzy processing time. An instance has been designed to test the performance of proposed algorithm. The experiment result shows that the proposed MOGA could obtain satisfactory Pareto solutions for the multi-objective IPPS problem with fuzzy processing time.

引用

页码：293 / 298

页数：6

共 50 条

[21] Genetic algorithm integrated with artificial chromosomes for multi-objective flowshop scheduling problems
Chang, Pei-Chann
Chen, Shih-Hsin
Fan, Chin-Yuan
Chan, Chien-Lung
APPLIED MATHEMATICS AND COMPUTATION, 2008, 205 (02) : 550 - 561
[22] MULTI-OBJECTIVE TRANSMISSION EXPANSION PLANNING USING FUZZY-GENETIC ALGORITHM
Shivaie, M.
Sepasian, M. S.
Sheikh-El-Eslami, M. K.
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING, 2011, 35 (E2) : 141 - 159
[23] Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem
Wen, Xiaoyu
Li, Xinyu
Gao, Liang
Wang, Kanghong
Li, Hao
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2020, 17 (03):
[24] A novel parallel multi-objective genetic algorithm and its application in process scheduling
Li, YJ
Wu, TJ
PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5, 2000, : 525 - 528
[25] Novel parallel multi-objective genetic algorithm for process industry production scheduling
Li, Y.J.
Wu, T.J.
2001, Systems Engineering Society of China (21):
[26] Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm
Mohapatra, P.
Nayak, A.
Kumar, S. K.
Tiwari, M. K.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2015, 53 (06) : 1712 - 1735
[27] An interactive algorithm for multi-objective flow shop scheduling with fuzzy processing time through resolution method and TOPSIS
Nakhaeinejad, Mahdi
Nahavandi, Nasim
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2013, 66 (5-8): : 1047 - 1064
[28] An interactive algorithm for multi-objective flow shop scheduling with fuzzy processing time through resolution method and TOPSIS
Mahdi Nakhaeinejad
Nasim Nahavandi
The International Journal of Advanced Manufacturing Technology, 2013, 66 : 1047 - 1064
[29] Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time
Li, Rui
Gong, Wenyin
Lu, Chao
COMPUTERS & INDUSTRIAL ENGINEERING, 2022, 168
[30] Multi-objective reactive scheduling based on genetic algorithm
Tanimizu, Yoshitaka
Miyamae, Tsuyoshi
Sakaguchi, Tatsuhiko
Iwamura, Koji
Sugimura, Nobuhiro
TOWARDS SYNTHESIS OF MICRO - /NANO - SYSTEMS, 2007, (05): : 65 - +

← 1 2 3 4 5 →