Use of a classifier in a knowledge-based simulation optimization system

This article defines and develops a simulation optimization system based upon response surface classification and the integration of multiple search strategies. Response surfaces are classified according to characteristics that indicate which search technique will be most successful. Typical surface characteristics include statistical measures and topological features, while search techniques encompass response surface methodology, simulated annealing, random search, etc. The classify-then-search process flow and a knowledge-based architecture are developed and then demonstrated with a detailed computer example. The system is useful not only as an approach to optimizing simulations, but also as a means for integrating search techniques and thereby providing the user with the most promising path toward an optimal solution. © 1995 John Wiley & Sons, Inc.     

Nonadjacent extreme point methods for solving linear programs

In this article we present some advanced basis or block-pivoting, relaxation, and feasible direction methods for solving linear programming problems. Preliminary computational results appear to indicate that the former two types of simplex-based procedures may hold promise for solving linear programming problems, unlike the third type of scheme which is shown to be computationally unattractive.