Suchergebnisse

AbstractSuppose we are given a network G=(V,E) with arc distances and a linear cost function for lengthening arcs. In this note, we consider a network‐interdiction problem in which the shortest path from source node s to sink node t is to be increased to at least τ units via a least‐cost investment strategy. This problem is shown to reduce to a simple minimum‐cost‐flow problem. Applications and generalizations are discussed, including the multiple‐destination case.

AbstractIn recent papers by Kirkpatrick et al., an analogy between the statistical mechanics of large multivariate physical systems and combinatorial optimization has been presented and used to develop a general strategy for solving discrete optimization problems. The method relies on probabilistically accepting intermediate increases in the objective function through a set of user‐controlled parameters. It is argued that by taking such controlled uphill steps, from time to time, a high quality solution can eventually be found in a moderate amount of computer time. In this paper, we implement this idea, apply it to the traveling salesman problem and the p‐median location problem, and test the approach extensively.

AbstractThe stochastic vehicle routing problem is a problem of current importance and research interest. Applications include schoolbus routing, municipal waste collection, subscription bus scheduling, daily delivery of dairy goods, and a host of related transportation and distribution activities. In this paper, we assume that routes for vehicles have already been generated and we focus on determining operating strategies. That is, under what conditions should a driver return to the central depot in order to replenish his supply? We present a dynamic programming recursion which addresses this question and we show that the optimal policy is of a rather simple form. Finally, an algorithm and example illustrate the policy.

AbstractConsider an "intractable" optimization problem for which no efficient solution technique exists. Given a systematic procedure for generating independent heuristic solutions, we seek to obtain interval estimates for the globally optimal solution using statistical inference. In previous work, accurate point estimates have been derived. Determining interval estimates, however, is a considerably more difficult task. In this paper, we develop straightforward procedures which compute confidence intervals efficiently in order to evaluate heuristic solutions and assess deviations from optimality. The strategy presented is applicable to a host of combinatorial optimization problems. The assumptions of our model, along with computational experience, are discussed.