A goal programming embedded genetic algorithm for multi-objective manufacturing cell design

被引：0

作者：

Chaudhuri B. ^{[1
]}

Jana R.K. ^{[2
]}

Sharma D.K. ^{[3
]}

Dan P.K. ^{[4
]}

机构：

[1] Department of Business Management, Indian Institute of Social Welfare and Business Management

[2] Indian Institute of Management Raipur, GEC Campus, Sejbahar

[3] Department of Business, Management and Accounting, University of Maryland Eastern Shore, Princess Anne, 21853, MD

[4] Rajendra Mishra School of Engineering Entrepreneurship, Indian Institute of Technology, Kharagpur, WB

来源：

International Journal of Applied Decision Sciences | 2019年 / 12卷 / 01期

关键词：

Genetic algorithm; Goal programming; Manufacturing cell design; Multi-objective optimisation;

D O I：

10.1504/IJADS.2019.096562

中图分类号：

学科分类号：

摘要：

In this paper, a multi-objective manufacturing cell design problem is studied. A goal programming (GP) embedded real-coded genetic algorithm (GA) is designed for solving this problem. Initially, the GA is used to obtain the individual minimum of each objective. Thereafter, utilising the concepts of GP, an equivalent problem is derived, and the sum of deviation variables associated with the objectives are minimised. The GA is used further to obtain the optimal cell design. A software toolkit is developed based on the proposed technique using C Sharp.net to ensure its use in a larger scale. The effectiveness of the technique is judged based on a set of test problems of different sizes. The proposed technique is found to be better in terms of the performance measure over the existing ones. Copyright © 2019 Inderscience Enterprises Ltd.

引用

页码：98 / 114

页数：16

共 50 条

[41] A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system [J].

Zeidi, Javad Rezaeian ;

Javadian, Nikbakhsh ;

Tavakkoli-Moghaddam, Reza ;

Jolai, Fariborz .

COMPUTERS & INDUSTRIAL ENGINEERING, 2013, 66 (04) :1004-1014

[42] Application of fuzzy goal programming approach to multi-objective linear fractional inventory model [J].

Dutta, D. ;

Kumar, Pavan .

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2015, 46 (12) :2269-2278

[43] Augmenting Multi-Objective Genetic Algorithm and Dynamic Programming for Online Coverage Path Planning [J].

Sadek, Mina G. ;

Mohamed, Amr E. ;

El-Garhy, Ahmed M. .

PROCEEDINGS OF 2018 13TH INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND SYSTEMS (ICCES), 2018, :475-480

[44] Genetic Algorithm Based Fuzzy Multi-objective Nonlinear Programming of Regional Water Allocation [J].

Li Shurong ;

Wang Feng ;

Ma Tao ;

Yan Wei .

2008 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2008, :157-162

[45] A fuzzy goal programming approach to multi-objective optimization problem with priorities [J].

Hu, Chao-Fang ;

Teng, Chang-Jun ;

Li, Shao-Yuan .

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2007, 176 (03) :1319-1333

[46] An Intuitionistic Fuzzy Multi-Objective Goal Programming Approach to Portfolio Selection [J].

Yu, Gao-Feng ;

Li, Deng-Feng ;

Liang, De-Cui ;

Li, Guang-Xu .

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2021, 20 (05) :1477-1497

[47] A Satisfactory Goal Programming and Analysis Method for Fuzzy Multi-Objective Optimization [J].

Hu Chaofang ;

Wang Na .

2011 30TH CHINESE CONTROL CONFERENCE (CCC), 2011, :2110-2114

[48] A Goal Programming Approach to Solve Multi-objective Chance Constrained Programming in Fuzzy Environment [J].

Beaula, Thangaraj ;

Seetha, R. .

COMMUNICATIONS IN MATHEMATICS AND APPLICATIONS, 2023, 14 (01) :203-213

[49] Genetic algorithm for multi-objective experimental optimization [J].

Hannes Link ;

Dirk Weuster-Botz .

Bioprocess and Biosystems Engineering, 2006, 29 :385-390

[50] A multi-objective micro genetic ELM algorithm [J].

Lahoz, David ;

Lacruz, Beatriz ;

Mateo, Pedro M. .

NEUROCOMPUTING, 2013, 111 :90-103

← 1 2 3 4 5 →