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A fully fuzzified, intelligent theory-of-constraints product-mix decision

A., Bhattacharya and P., Vasant and B., Sarkar and S.K., Mukherjee (2008) A fully fuzzified, intelligent theory-of-constraints product-mix decision. [Citation Index Journal]

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Abstract

The present research work outlines a fuzzified approach using fuzzy linear programming (FLP) using a suitably designed smooth logistic membership function (MF) for finding fuzziness patterns at disparate levels of satisfaction for theory of constraints-based (TOC) product-mix decision problems. The objective of the present work is to find fuzziness patterns of product-mix decisions with disparate levels of satisfaction of the decision-maker (DM). Another objective is to provide a robust, quantified monitor of the level of satisfaction among DMs and to calibrate these levels of satisfaction against DM expectations. Product-mix decision should take into account considerations such as the DM's level of satisfaction (sometimes called 'emotions') in order to make the decision a robust one. Sensitivity of the decision has been focused on a bottleneck-free, optimal product-mix solution of a TOC problem. The inefficiency of traditional linear programming (LP) in handling multiple-bottleneck problems using TOC is discussed using an illustrative example. Relationships among the degree of fuzziness, level of satisfaction and the throughput of modified TOC guide decision-makers (DM) under tripartite fuzzy environment in obtaining their product-mix choice trading-off with a pre-determined allowable fuzziness.

Item Type:Citation Index Journal
Uncontrolled Keywords:Decision theory; Intelligent systems; Problem solving; Robustness (control systems); Fuzzified linear programming; Intelligent decision; Constraint theory
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:316
Deposited By: Mr Helmi Iskandar Suito
Deposited On:04 Mar 2010 02:35
Last Modified:19 Jan 2017 08:26

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