Constructing Risk Measures from Uncertainty Sets

We propose a unified theory that links uncertainty sets in robust optimization to risk measures in portfolio optimization. We illustrate the correspondence between uncertainty sets and some popular risk measures in finance, and show how robust optimization can be used to generalize the concepts of these measures. We also show that by using properly defined … Read more

A Random Key Based Genetic Algorithm for the Resource Constrained Project Scheduling Problem

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach … Read more

Non-Linear Stochastic Fractional Programming Models of Financial Derivatives

Non-Linear Stochastic Fractional programming models provide numerous insights into a wide variety of areas such as in financial derivatives. Portfolio optimization has been one of the important research fields in modern finance. The most important character within this optimization problem is the uncertainty of the future returns on assets. The objective of this study is … Read more

Optimal Information Monitoring Under a Politeness Constraint

We describe scheduling algorithms for monitoring an information source whose contents change at times modeled by a nonhomogeneous Poisson process. In a given time period of length T, we enforce a politeness constraint that we may only probe the source at most n times. This constraint, along with an optional constraint that no two probes … Read more

Compact linearization for bilinear mixed-integer problems

We present a compact linearization for a broad class of bilinear 0-1 mixed-integer problems subject to assignment constraints. We apply the linearization to three classes of problems: quadratic assignment, multiprocessor scheduling with communication delays, and graph partitioning, and show that it yields faster solution times. Citation DEI, Politecnico di Milano, Working paper, April 2005. Article … Read more

A Lagrangean Relaxation and Decomposition Algorithm for the Video Placement and Routing Problem

Video on Demand (VoD) is a technology used to provide a number of programs to a number of users on request. In developing a VoD system, a fundamental problem is load balancing, which is further characterized by optimally placing videos to a number of predefined servers and routing the user program requests to available resources. … Read more

Preemptive scheduling with position costs

This paper is devoted to basic scheduling problems in which the scheduling cost of a job is not a function of its completion time. Instead, the cost is derived from the integration of a cost function over the time intervals on which the job is processed. This criterion is specially meaningful when job preemption is … Read more

The value of multi-stage stochastic programming in capacity planning under uncertainty

This paper addresses a general class of capacity planning problems under uncertainty, which arises, for example, in semiconductor tool purchase planning. Using a scenario tree to model the evolution of the uncertainties, we develop a multi-stage stochastic integer programming formulation for the problem. In contrast to earlier two-stage approaches, the multi-stage model allows for revision … Read more

Solving Multi-Leader-Follower Games

Multi-leader-follower games arise when modeling competition between two or more dominant firms and lead in a natural way to equilibrium problems with equilibrium constraints (EPECs). We examine a variety of nonlinear optimization and nonlinear complementarity formulations of EPECs. We distinguish two broad cases: problems where the leaders can cost-differentiate and problems with price-consistent followers. We … Read more

Robust DWDM Routing and Provisioning under Polyhedral Demand Uncertainty

We present mixed integer linear programming models that are robust in the face of uncertain traffic demands known to lie in a certain polyhedron for the problem of dense wavelength division multiplexing network routing and provisioning at minimal cost. We investigate the solution of the problem in a set of numerical experiments for two models … Read more