This paper discusses the fuzzy decision making under the theory-of-constraints (TOC) for the product-mix problem. It utilizes a smooth logistic membership function for finding out the fuzziness patterns in the disparate level of satisfaction. This membership function has been validated for applying to the real-world product-mix problems. This contribution provides a robust, quantified monitoring of the level of satisfaction among the decision makers, and calibrates these levels of satisfaction against the decision-makers expectations. It thus provides a computational intelligence procedure. The solution by Hsu and Chung via the dominance-rule technique yields the optimal throughput as US 11,873, which is identical to the solution found by the fuzzy-logic-programming (FLP) methodology, and is considered to be better suited for the product-mix-selection problems. The flexibility of the model lies in the membership function design. The chapter concludes by discussing the inefficiency of the traditional linear programming in handling the multiple bottleneck problems, through the TOC concept via an illustrative example. © 2006 Elsevier Ltd All rights reserved.