We study a generalized distributionally robust chance-constrained set covering problem (DRC) with Wasserstein ambiguity set, where both decisions and uncertainty are binary-valued. establish the NP-hardness of DRC recast it as two-stage stochastic program, which facilitates decomposition algorithms. Furthermore, we derive two families valid inequalities. The first family targets hypograph “shif...

In this paper, we use an input oriented chance-constrained DEA model withrandom inputs and outputs. A super-eciency model with chance constraintsis used for ranking. However, for convenience in calculations a non-linear deterministicequivalent model is obtained to solve the models. The non-linearmodel is converted into a model with quadratic constraints to solve the nonlineardeterministic model...

In this paper, a model of an optimal control problem with chance constraints is introduced. The parametersof the constraints are fuzzy, random or fuzzy random variables. Todefuzzify the constraints, we consider possibility levels.  Bychance-constrained programming the chance constraints are converted to crisp constraints which are neither fuzzy nor stochastic and then the resulting classical op...

There are varieties of QFD combination forms available that can help management to choose the right model for his/her types of problem. The proposed MOCC-QFD-FMEA model is a right model to include variety of objectives as well as the risk factors into the model of the problem. Due to the fact that the model also takes into consideration the concept of Fuzzy set, it further allows management...

This paper addresses a new version of the exible ow line prob- lem, i.e., the budget constrained one, in order to determine the required num- ber of processors at each station along with the selection of the most eco- nomical process routes for products. Since a number of parameters, such as due dates, the amount of available budgets and the cost of opting particular routes, are imprecise (fuzz...

In the era of modern business analytics, data-driven optimization has emerged as a popular modeling paradigm to transform data into decisions. By constructing an ambiguity set potential data-generating distributions and subsequently hedging against all member within this set, effectively combats with which real-life sets are plagued. Chen et al. (2022) study data-driven, chance-constrained prog...