We present a modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems. Like the original simulated annealing algorithm, our method has the hill climbing feature, so it can find global optimal solutions to discrete stochastic optimization...

In this paper, we present a novel quantitative analysis for the strategic planning decision problem of allocating certain available prevention and protection resources to, respectively, reduce the failure probabilities of system safety measures and the total expected loss from a sequence of...

We describe a family of heuristics to solve combinatorial problems such as routing and partitioning. These heuristics exploit geometry but ignore specific distance measures. Consequently they are simple and fast, but nonetheless fairly accurate, and so seem well-suited to operational problems...

One of the most computationally useful ideas of the 1970s is the observation that many hard integer programming problems can be viewed as easy problems complicated by a relatively small set of side constraints. Dualizing the side constraints produces a Lagrangian problem that is easy to solve...

A new approximate algorithm for multidimensional zero-one knapsack problems with all positive coefficients is presented. The procedure is controlled by three parameters which affect the tradeoff between solution quality and computation time and whose values are set by the users. For 48 test...