Consideration of quality implicitly introduces the need to adjust inputs in order to obtain desired output. Output evaluation must consider quality as well as cost. Real decisions may involve other objectives as well. Production problems often involve a dynamic situation where the relationship between cost and quality must be experimentally developed. The proposed method is to use regression as...

Quality function deployment (QFD) is a method used for the manufacturing process of a product or service that is devoted to transforming customer requirements (CRs) into appropriate engineering characteristics (ECs) by specifying the importance of the ECs and then setting their target values. Confronting the inherent vagueness or impreciseness in the QFD process, we embed the fuzzy set theory i...

In this paper, we study the linear fractional transportation problem with uncertain arameters. After recalling some definitions, concepts and theorems in uncertainty theory we present three approaches for solving this problem. First we consider the expected value of the objective function together with the expectation of satisfying constraints. Optimizing the expected value of the objective fun...

Supply Chain (SC) design problems are often characterized with uncertainty related to the decisionmaking parameters. The Stochastic Goal Programming (SGP) was one of the aggregating procedures proposed to solve the SC problems. However, the SGP does not integrate explicitly the Decision-Maker’s preferences. The aim of this paper is to utilize the Chance Constrained Programming and the Satisfact...

A lower bound for a finite-scenario chance-constrained problem is given by the quantile value corresponding to the sorted optimal objective values of scenario subproblems. This quantile bound can be improved by grouping subsets of scenarios at the expense of larger subproblems. The quality of the bound depends on how the scenarios are grouped. We formulate a mixed-integer bilevel program that o...

The main focus of this paper is in a discussion of complexity of stochastic programming problems. We argue that two-stage (linear) stochastic programming problems with recourse can be solved with a reasonable accuracy by using Monte Carlo sampling techniques, while multi-stage stochastic programs, in general, are intractable. We also discuss complexity of chance constrained problems and multi-s...