Due to a wide variety of traffic requirements, the design, dimensioning and traffic management of Broadband Integrated Services Digital Networks (B-ISDN) involves the application of diverse methods and theories. In this paper we formulate a model which aims to combine results on the probabilistic analysis of congestion in the network with optimisation of traffic routing decisions. The optimizat...

The paper deals with two methods of solving optimization programs whereuncertainties occur: stochastic (in particular chance-constrained) programming androbust programming. We review briefly how these two methods deal with uncertaintyand what approximations are commonly used. Furthermore, we are concentrated onapproximations based on sample sets where some type of weak dependenc...

Project scheduling problem is to determine the schedule of allocating the loans to a project in order to balance the total cost and the completion time of the project under some constraints. This paper considers project scheduling problem in an uncertain environment, of which the duration time of each activity is an uncertain variable. Then uncertain chance-constrained programming model is cons...

The mixing set with a knapsack constraint arises in deterministic equivalent of chance-constrained programming problems with finite discrete distributions. We first consider the case that the chance-constrained program has equal probabilities for each scenario. We study the resulting mixing set with a cardinality constraint and propose facet-defining inequalities that subsume known explicit ine...

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...

Uncertainty issues associated with a multi-site, multi-product supply chain planning problem has been analyzed in this paper using the chance constraint programming approach. In literature, such problems have been addressed using the two stage stochastic programming approach. While this approach has merits in terms of decomposition, computational complexity even for small size planning problem ...

In this paper, “chance optimization” problems are introduced, where one aims at maximizing the probability of a set defined by polynomial inequalities. These problems are, in general, nonconvex and computationally hard. With the objective of developing systematic numerical procedures to solve such problems, a sequence of convex relaxations based on the theory of measures and moments is provided...

Deterministic optimization approaches have been well developed and widely used in the process industry to accomplish off-line and on-line process optimization. The challenging task for the academic research currently is to address large-scale, complex optimization problems under various uncertainties. Therefore, investigations on the development of stochastic optimization approaches are necessi...

The wide applicability of chance–constrained programming, together with advances in convex optimization and probability theory, has created a surge of interest in finding efficient methods for processing chance constraints in recent years. One of the successes is the development of so–called safe tractable approximations of chance–constrained programs, where a chance constraint is replaced by a...