In this paper, a Multi-Choice Stochastic Bi-Level Programming Problem (MCSBLPP) is considered where all the parameters of constraints are followed by normal distribution. The cost coefficients of the objective functions are multi-choice types. At first, all the probabilistic constraints are transformed into deterministic constraints using stochastic programming approach. Further, a general tran...

a linear programming model has been presented for finding an appropriate planning in order to maximize hotel revenue. in this model, a special planning horizon has been considered that includes several busy days of a year. there are several reservation periods that may start a few months earlier. each period of reservation may have different prices and so result different incomes. hotel custome...

drought is a gradual phenomenon and cause important changes in water resources, agriculture and so on. due to the stochastic behavior of factors that influence the occurrence and intense of drought, one can consider this phenomenon as a stochastic process. with respect to the importance of predicted drought severity and its vital influence in designing and operating water resources systems, thi...

Existence of capacity drop phenomenon, as the difference between pre-queue and queue discharge flow rates, has been one of the controversial concepts of traffic engineering. Several researches have focused on capacity drop existence and also its estimation issues. This paper aims to estimate capacity drop based not only on a comparison between breakdown and queue discharge flow rates, but also ...

Stochastic Approach to Vehicle Routing Problem: Development and Theories Abstract In this article, a chance constrained (CCP) formulation of the Vehicle Routing Problem (VRP) is proposed. The reality is that once we convert some special form of probabilistic constraint into their equivalent deterministic form then a nonlinear constraint generates. Knowing that reliable computer software...

In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic difficulty of solving such stochastic optimization problems is that the involved multidimensional integrals (expectations) cannot be computed with high accuracy. The aim of this paper is to compare two computational approaches based on Monte Carlo sampling techniques, name...