Computational studies on two-stage stochastic programming problems indicate that aggregate models have better scale-up properties than disaggregate ones, though the threshold of breaking even may be high. In this paper we attempt to explain this phenomenon, and to lower this threshold. We present the on-demand accuracy approach of Oliveira and Sagastizábal in a form which shows that this approa...

Shortest path problem is a fundamental problem in network optimization and combinational optimization. The existing literature mainly concerned with the problem in deterministic, stochastic or fuzzy environments by using different tools. Different from the existing works, we investigate shortest path problem by regarding arc lengths as uncertain variables which are employed to describe the beha...

  This paper concerns the problem of robust stabilization of uncertain fractional-order non-autonomous systems. In this regard, a single input active control approach is proposed for control and stabilization of three-dimensional uncertain fractional-order systems. The robust controller is designed on the basis of fractional Lyapunov stability theory. Furthermore, the effects of model uncertai...

Oil prices remained relatively low but volatile in the 2015-17 period, largely due to declining and uncertain demand from China. This follows a prolonged decline from around $110 per barrel in June 2014 to below $30 in January 2016, due in large part to increased supply of shale oil in the US, which was spurred by the development of fracking technology. Most dynamic Cournot models focus on supp...

We present a technique for approximate robust dynamic programming that is suitable for linearly constrained polytopic systems with piecewise affine cost functions. The approximation method uses polyhedral representations of the cost-to-go function and feasible set, and can considerably reduce the computational burden compared to recently proposed methods for exact robust dynamic programming [Be...

This paper presents an optimization model for planning tool purchases for a semiconductor manufacturing facility under uncertain operating conditions. By modeling the uncertain parameters using a scenario tree, we develop a stochastic programming formulation for the problem. In contrast to earlier two-stage approaches for this problem, our model allows for revision of the tool purchase plan as ...

This tutorial is aimed at introducing some basic ideas of stochastic programming. The intended audience of the tutorial is optimization practitioners and researchers who wish to acquaint themselves with the fundamental issues that arise when modeling optimization problems as stochastic programs. The emphasis of the paper is on motivation and intuition rather than technical completeness (althoug...

perishability of blood products as well as uncertainty in demand amounts complicate the management of blood supply for blood centers. this paper addresses a mixed-integer linear programming model for blood platelets production planning while integrating the processes of blood collection as well as production/testing, inventory control and distribution. whole blood-derived production methods for...

In this paper, we propose a new methodology for handling optimization problems with uncertain data. With the usual Robust Optimization paradigm, one looks for the decisions ensuring a required performance for all realizations of the data from a given bounded uncertainty set, whereas with the proposed approach, we require also a controlled deterioration in performance when the data is outside th...