The present work is intended as a first step towards applying semidefinite programming models and tools to discrete lot-sizing problems including sequence-dependent changeover costs and times. Such problems can be formulated as quadratically constrained quadratic binary programs. We investigate...

In this paper, we discuss the optimal design of telecommunications networks prone to failure. The problem concerns mid-range planning objectives. The purpose of the model is to give insight on the cost of protecting the network against failures by balancing the choice of a base routing of the...

This paper deals with the sizing of telecommunications networks offering private line service to a few clients. The clients ask for some transfer capacity between some pair of nodes, but their demand is uncertain. In case of high demand and insufficient capacity, some clients may be denied the...

In this paper we present branch-and-bound (B& B) strategies for two-stage stochastic integer network design-based models with integrality constraints in the first-stage variables. These strategies are used within L-shaped decomposition-based B& B framework. We propose a valid inequality in order...

This paper deals with stochastic scheduling of nuclear power plant outages. Focusing on the main constraints of the problem, we propose a stochastic formulation with a discrete distribution for random variables, that leads to a mixed 0/1 quadratically constrained quadratic program. Then we...

General multiperiod optimal consumption and investment problems with proportional transaction costs are investigated in this paper, a <Emphasis Type="Bold">GARCH-type process is used to model the risky asset’s return series so that its time-varying moments and conditional heteroskedasticity can be properly...</emphasis>

The mean-risk stochastic mixed-integer programs can better model complex decision problems under uncertainty than usual stochastic (integer) programming models. In order to derive theoretical results in a numerically tractable way, the contamination technique is adopted in this paper for the...

The mean-risk stochastic mixed-integer programs can better model complex decision problems under uncertainty than usual stochastic (integer) programming models. In order to derive theoretical results in a numerically tractable way, the contamination technique is adopted in this paper for the...