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

Bhattacharya, Arijit ORCID: https://orcid.org/0000-0001-5698-297X, Vasant, Pandian, Sarkar, Bijan and Mukherjee, Sanat Kumar (2008) A fully fuzzified, intelligent theory-of-constraints product-mix decision. International Journal of Production Research, 46 (3). pp. 789-815. ISSN 0020-7543

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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: Article
Uncontrolled Keywords: degree of fuzziness,fuzzified linear programming,intelligent decision,level of satisfaction,toc,strategy and management,management science and operations research,industrial and manufacturing engineering ,/dk/atira/pure/subjectarea/asjc/1400/1408
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Depositing User: LivePure Connector
Date Deposited: 05 May 2020 00:06
Last Modified: 22 Oct 2022 06:06
URI: https://ueaeprints.uea.ac.uk/id/eprint/74977
DOI: 10.1080/00207540600823187

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