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A soft-computing approach to multi-item fuzzy EOQ model incorporating discount

A soft-computing approach to multi-item fuzzy EOQ model incorporating discount Contractive mapping genetic algorithm (CMGA) with logic structures has been developed and implemented for multi-item EOQ models with all unit discount (AUD), incremental quantity discount (IQD) and a combination of these discounts having fuzzy objective goal and resources. Here, AUD or/and IQD with two price breaks on purchasing price are allowed. For storage, warehouse capacity is limited but imprecise in nature. Selling price is mark-up of the purchasing cost. Profit function is formulated for the system incorporating impreciseness in it and is maximised using CMGA. The impreciseness in storage space and profit goal has been represented by fuzzy linear membership functions. For the present model, CMGA has been developed in real code representation and has been successfully implemented to obtain the optimum order quantities for the fuzzy inventory model with price-breaks in order to achieve the maximum profit. Numerical examples are provided to illustrate the model and sensitivity analyses with respect to different demand have been performed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

A soft-computing approach to multi-item fuzzy EOQ model incorporating discount

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2012.050376
Publisher site
See Article on Publisher Site

Abstract

Contractive mapping genetic algorithm (CMGA) with logic structures has been developed and implemented for multi-item EOQ models with all unit discount (AUD), incremental quantity discount (IQD) and a combination of these discounts having fuzzy objective goal and resources. Here, AUD or/and IQD with two price breaks on purchasing price are allowed. For storage, warehouse capacity is limited but imprecise in nature. Selling price is mark-up of the purchasing cost. Profit function is formulated for the system incorporating impreciseness in it and is maximised using CMGA. The impreciseness in storage space and profit goal has been represented by fuzzy linear membership functions. For the present model, CMGA has been developed in real code representation and has been successfully implemented to obtain the optimum order quantities for the fuzzy inventory model with price-breaks in order to achieve the maximum profit. Numerical examples are provided to illustrate the model and sensitivity analyses with respect to different demand have been performed.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2012

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