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Peer review of research proposals and articles is an essential element in research and development processes worldwide. Here we consider a problem that, to the best of our knowledge, has not been addressed until now: how to assign subsets of proposals to reviewers in scenarios where the reviewers supply their evaluations through ordinal ranking. The solution approach we propose for this assignment problem maximizes the number of proposal pairs that will be evaluated by one or more reviewers. This new approach should facilitate meaningful aggregation of partial rankings of subsets of proposals by multiple reviewers into a consensus ranking. We offer two ways to implement the approach: an integer-programming set-covering model and a heuristic procedure. The effectiveness and efficiency of the two models are tested through an extensive simulation experiment.

The Generalized Traveling Salesman Problem (GTSP) is stated as follows. Given a weighted complete digraph K * n and a partition V1, . . . , V k of its vertices, find a minimum weight cycle containing exactly one vertex from each set Vi, i = 1, . . . , k. We study transformations from GTSP to TSP. The 'exact' Noon-Bean transformation is investigated in computational experiments. We study the 'non-exact' Fischetti-Salazar-Toth (FST) transformation and its two modifications in computational experiments and theoretically using domination analysis. One of our conclusions is that one of the modifications of the FST transformation is better than the original FST transformation in the worst case in terms of domination analysis.

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Peer review of research proposals and articles is an essential element in R&D processes world-wide. In most cases, each reviewer evaluates a small subset of the candidate proposals.The review board is then faced with the challenge of creating an overall "consensus" ranking on the basis of many partial rankings. In this paper we propose a branch and bound model to support the construction of an aggregate ranking from the partial rankings provided by the reviewers. In a recent paper we proposed ways to allocate proposals to reviewers so as to achieve the maximum possible overlap among the subsets of proposals allocated to different reviewers. Here, we develop a special branch and bound algorithm that utilizes the overlap generated through our earlier methods to enable discrimination in ranking the competing proposals. The effectiveness and efficiency of the algorithm is demonstrated with small numerical examples and tested through an extensive simulation experiment.

We define a new class of games, congestion games with loaddependent failures (CGLFs), which generalizes the well-known class of congestion games, by incorporating the issue of resource failures into congestion games. In a CGLF, agents share a common set of resources, where each resource has a cost and a probability of failure. Each agent chooses a subset of the resources for the execution of his task, in order to maximize his own utility. The utility of an agent is the difference between his benefit from successful task completion and the sum of the costs over the resources he uses. CGLFs possess two novel features. It is the first model to incorporate failures into congestion settings, which results in a strict generalization of congestion games. In addition, it is the first model to consider load-dependent failures in such framework, where the failure probability of each resource depends on the number of agents selecting this resource. Although, as we show, CGLFs do not admit a potential function, and in general do not have a pure strategy Nash equilibrium, our main theorem proves the existence of a pure strategy Nash equilibrium in every CGLF with identical resources and nondecreasing cost functions.

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A military arms race is characterized by an iterative development of measures and countermeasures. An attacker attempts to introduce new weapons in order to gain some advantage, whereas a defender attempts to develop countermeasures that can mitigate or even eliminate the effects of the weapons. This paper addresses the defender's decision problem: given limited resources, which countermeasures should be developed and how much should be invested in their development to minimize the damage caused by the attacker's weapons over a certain time horizon. We formulate several optimization models, corresponding to different operational settings, as constrained shortest-path problems and variants thereof. We then demonstrate the potential applicability and robustness of this approach with respect to various scenarios.