Robust Rankings for College Football

We investigate the sensitivity of the Colley Matrix (CM) rankings—one of six computer rankings used by the Bowl Championship Series—to (hypothetical) changes in the outcomes of (actual) games. Specifically, we measure the shift in the rankings of the top 25 teams when the win-loss outcome of, say, a single game between two teams, each with … Read more

Daily Scheduling of Nurses in Operating Suites

This paper provides a new multi-objective integer programming model for the daily scheduling of nurses in operating suites. The model is designed to assign nurses to di erent surgery cases based on their specialties and competency levels, subject to a series of hard and soft constraints related to nurse satisfaction, idle time, overtime, and job changes … Read more

Subspace accelerated matrix splitting algorithms for bound-constrained quadratic programming and linear complementarity problems

This paper studies the solution of two problems—bound-constrained quadratic programs and linear complementarity problems—by two-phase methods that consist of an active set prediction phase and a subspace phase. The algorithms enjoy favorable convergence properties under weaker assumptions than those assumed for other methods in the literature. The active set prediction phase employs matrix splitting iterations … Read more

Improving Robust Rolling Stock Circulation in Rapid Transit Networks

The routing of the rolling stock depends strongly on the rolling stock assignment to di erent opera- tions and the shunting schedule. Therefore, the integration of these decision making is justi ed and is appropriate to introduce robustness in the model. We propose a new approach to obtain better circula- tions of the rolling stock material, solving … Read more

Stochastic approaches for solving Rapid Transit Network Design models with random demand

We address rapid transit network design problems characterized by uncertainty in the input data. Network design has a determinant impact on the future e ective- ness of the system. Design decisions are made with a great degree of uncertainty about the conditions under which the system will be required to operate. The de- mand is one … Read more

Managing Operational and Financing Decisions to Meet Consumption Targets

We study dynamic operational decision problems where risky cash flows are being resolved over a finite planning horizon. Financing decisions via lending and borrowing are available to smooth out consumptions over time with the goal of achieving some prescribed consumption targets. Our target-oriented decision criterion is based on the aggregation of Aumann and Serrano (2008) … Read more

On Kusuoka representation of law invariant risk measures

In this paper we discuss representations of law invariant coherent risk measures in a form of integrals of the Average Value-at-Risk measures. We show that such integral representation exists iff the dual set of the considered risk measure is generated by one of its elements, and this representation is uniquely defined. On the other hand, … Read more

Robust management and pricing of LNG contracts with cancellation options

Liquefied Natural Gas contracts offer cancellation options that make their pricing difficult, especially if many gas storages need to be taken into account. We develop a valuation mechanism for such contracts from the buyer’s perspective, a large gas company whose main interest in these contracts is to provide a reliable supply of gas to its … Read more

Decomposition methods based on projected gradient for network equilibrium problems

In this work we consider the symmetric network equilibrium problem formulated as convex minimization problem whose variables are the path flows. In order to take into account the difficulties related to the large dimension of real network problems we adopt a column generation strategy and we employ a gradient projection method within an inexact decomposition … Read more

A General Framework for Designing Approximation Schemes for Combinatorial Optimization Problems with Many Objectives Combined Into One

In this paper, we present a general framework for designing approximation schemes for combinatorial optimization problems in which the objective function is a combination of more than one function. Examples of such problems include those in which the objective function is a product or ratio of two linear functions, parallel machine scheduling problems with the … Read more