Multi-item fuzzy EOQ models using genetic algorithm

A soft computing approach is proposed to solve non-linear programming problems under fuzzy objective goal and resources with/without fuzzy parameters in the objective function. It uses genetic algorithms (GAs) with mutation and whole arithmetic crossover. Here, mutation is carried out along the weig...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 44; no. 1; pp. 105 - 117
Main Authors: Mondal, S., Maiti, M.
Format: Journal Article
Language:English
Published: Seoul Elsevier Ltd 2003
Oxford Pergamon Press
New York, NY Pergamon Press Inc
Subjects:
Algorithms
Applied sciences
Chi-square distribution
Erlang distribution
Exact sciences and technology
Fuzzy inventory
Fuzzy logic
Fuzzy non-linear programming
Genetic algorithm
Genetic algorithms
Inventory control
Inventory control, production control. Distribution
Linear programming
Mathematical models
Mutation
Nonlinear programming
Operational research and scientific management
Operational research. Management science
Soft computing
Studies
Fuzzy non-linear programming
Fuzzy inventory
Soft computing
Genetic algorithm
Chi-square distribution
Erlang distribution
Decision making
Non linear programming
Chi- square distribution
Chi square
Fuzzy logic
Economic order quantity
Inventory control
Incomplete information
Inventory
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:A soft computing approach is proposed to solve non-linear programming problems under fuzzy objective goal and resources with/without fuzzy parameters in the objective function. It uses genetic algorithms (GAs) with mutation and whole arithmetic crossover. Here, mutation is carried out along the weighted gradient direction using the random step lengths based on Erlang and Chi-square distributions. These methodologies have been applied to solve multi-item fuzzy EOQ models under fuzzy objective goal of cost minimization and imprecise constraints on warehouse space and number of production runs with crisp/imprecise inventory costs. The fuzzy inventory models have been formulated as fuzzy non-linear decision making problems and solved by both GAs and fuzzy non-linear programming (FNLP) method based on Zimmermann's approach. The models are illustrated numerically and the results from different methods are compared.
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ISSN:0360-8352
1879-0550
DOI:10.1016/S0360-8352(02)00187-0