In this study, an integrated fuzzy-stochastic linear programming model is developed and applied to municipal solid waste management. Methods of chance-constrained programming and fuzzy linear programming are incorporated within a general interval-parameter mixedinteger linear programming framework. It improves upon the existing optimization methods with advantages in uncertainty reflection, dat...

We propose a safe approximation to joint chance-constrained programming, where the constraint functions are additively dependent on normally-distributed random vector. The is analytical, meaning that it requires neither numerical integrations nor sampling-based probability approximations. Under mild assumptions, standard nonlinear program. compare this new another analytical for programming bas...

Various applications in reliability and risk management give rise to optimization problems with constraints involving random parameters, which are required to be satisfied with a pre-specified probability threshold. There are two main difficulties with such chance-constrained problems. First, checking feasibility of a given candidate solution exactly is, in general, impossible since this requir...

We study here a problem of scheduling n job types on m parallel machines, when setups are required and the demands for the products are correlated random variables. We model this problem as a chance constrained integer program. Methods of solution currently available { in integer programming and stochastic programming { are not suucient to solve this model exactly. We develop and introduce here...

This paper presents a chance constrained information gap decision model for multi-period microgrid expansion planning (MMEP) considering two categories of uncertainties, namely random and non-random uncertainties. The main task of MMEP is to determine the optimal sizing, type selection, and installation time of distributed energy resources (DER) in microgrid. In the proposed formulation, inform...