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

The present research work outlines a fuzzified approach using fuzzy linear programming using a suitably designed smooth logistic membership function for finding fuzziness patterns at disparate levels of satisfaction for theory of constraints-based 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.