This paper describes two experiments exploring the potential of the Kriging methodology for constrained simulation optimization. Both experiments study an (s, S) inventory system with the objective of finding the optimal values of s and S. The goal function and constraints in these two experiments differ, as does the approach to determine the optimum combination predicted by the Kriging model. The results of these experiments indicate that Kriging offers opportunities for solving constrained optimization problems in stochastic simulation; future research will focus on further refining the methodology.

successfully to determine intermediate steady state temperatures. Predicted behavior was found to be in reasonable agreement with experimental results.The initial rate of change of reactor temperature due to disturbances in inlet temperature and/or concentration was faster than the change following a perturbation in flow rate, causing difficulties in bringing the reactor back to its original condition. The closeness of the operating mean residence time to the corresponding critical value was found to be an important factor in this regard. The reactor performance was very sensitive to disturbances near the critical mean residence time.

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This paper describes a Java-based system for allocating simulation trials to a set of P parallel processors for carrying out a simulation study involving direct-search optimization or response surface methodology. Unlike distributed simulation, where a simulation model is decomposed and its parts run in a parallel environment, the parallel replications approach allows a simulation model to run to completion with a unique set of input conditions. Since a simulation study typically involves executing R replications of the model at each of S sets of input conditions, the server's task in managing a parallel replications approach is to allocate the RS x simulation trials to P client processors in a manner that balances the workload on those processors. The objective is to complete the simulation study in a time interval approaching 1/P of that which would be required of a single processor operating in a purely sequential mode. Results are reported for several Silk-based simulation models run in a Visual Café environment for Java.

This paper describes the application of Kriging metamodeling in multiple-objective simulation optimization. An Arenabased simulation model of an (s, S) inventory system is utilized to demonstrate the capabilities of Kriging metamodeling as a simulation tool. Response surface methodology and Kriging metamodeling are compared to determine the situations in which one approach might be preferred over the other. The optimization approaches described here have the objective of finding the optimal values of reorder point s and maximum inventory level S so as to minimize the total cost of the inventory system while maximizing customer satisfaction. This paper describes two alternative approaches to utilizing Kriging methodology with multiple-objective optimization in simulation studies.

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ABST RAG TThis paper examines a gradient search procedure for simulation experimentation with constrained systems.This procedure combines gradient search with curvilinear regression in moving toward a constrained optimal solution for a system involving n controllable variables.In a directiondetermining block, at least n+l simulation trials are performed around a current base point to establish an improving direction, Then in a step determining block, t simulation trials are performed along the improving direction to establish the most favorable step in moving to the next base point. This sequential block process, in which each block is executed in one input to the computer, is repeated until an approximate solution is found which satisfies all system constraints.