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In this paper we consider a loss system where the arrivals can be classified into different groups according to their arrival rate and expected service time. While the standard admission policy consists of rejecting only those customers who arrive when all servers are busy, we address the problem of finding the optimal static admission policy (with respect to a given reward structure) when customers can be discriminated according to the group they belong to, thus customers of some groups might be automatically rejected (even if some servers remain idle) in order to enhance the global efficiency of the system. The optimality of a c\mu -rule is shown, from which finite-time algorithms for the one- and two-server cases are derived.Algorithms, Multichannel Queues, Nonlinear Programming, Optimization

In this paper we address a generalization of the Weber problem, in which we seek for the center and the shape of a rectangle (the facility) minimizing the average distance to a given set (the demand-set) which is not assumed to be finite. Some theoretical properties of the average distance are studied, and an expression for its gradient, involving solely expected distances to rectangles, is obtained. This enables the resolution of the problem by standard optimization techniques.

The multicommodity-flow problem arises in a wide variety of important applications. Many communications, logistics, manufacturing, and transportation problems can be formulated as large multicommodity-flow problems. During the last few years researchers have made steady advances in solving extremely large multicommodity-flow problems. This improvement has been due both to algorithmic and to hardware advances. At present the primal simplex method using the basis-partitioning approach gives excellent solution times even on modest hardware. These results imply that we can now efficiently solve the extremely large multicommodity-flow models found in industry. The extreme-point solution can also be quickly reoptimized to meet the additional requirements often imposed upon the continuous solution. Currently practitioners are using EMNET, a primal basis-partitioning algorithm, to solve extremely large logistics problems with more than 600,000 constraints and 7,000,000 variables in the food industry.