Soft-sensing of level of satisfaction in a theory of constraints product-mix decision heuristic using robust fuzzy-LP

Product-mix decision through theory of constraints (TOC) should take into account considerations like the decision-maker's (DM) level of satisfaction in order to make product-mix decision a robust one. Sensitivity of the decision made, needs to be focused for a bottle-neck-free, optimal product-mix solution of TOC problem. A membership function (MF) has been suitably designed in the present work, first in finding out the degree of imprecision in the product-mix decision, and thereafter to sense the level of satisfaction of the DM. Inefficiency of traditional linear programming (LP) in handling multiple-bottleneck problem through TOC has been discussed through an illustrative example. Comparison of traditional LP over fully fuzzified-LP (FLP) model has also been addressed to elucidate the advantages of FLP in TOC. Key objective of this work is to guide DMs in finding out the optimal product-mix with higher degree of satisfaction with lesser degree of fuzziness under tripartite fuzzy environment.