A method for discrete stochastic optimization

Article Abstract:

A new method for discrete optimization is presented as a solution to the problem of optimizing a function over some set of feasible parameter values in cases where the objective function can only be measured or estimated but not analytically evaluated. The study focuses on the situation where a simulation program is used to evaluate the objective function. Two versions of a new iterative method for solving such a problem are introduced. Both versions involve the selection of a neighbor of the 'current' alternative and the comparison of the estimates of the objective function assessed the current and neighboring alternatives. One of the versions is proven to be a workable solution to discrete optimization problems with the objective function evaluated through transient or steady-state simulation. The other version is found to be an acceptable solution to a special class of discrete stochastic optimization problems.

Simulation methods, Simulation, Markov processes, Random walks (Mathematics), Random walk theory

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A simulated annealing algorithm with constant temperature for discrete stochastic optimization

Article Abstract:

An analysis considers the issue of optimizing an objective function over a discrete set of feasible solutions in cases where the objective function values cannot precisely be assessed and are, instead, estimated using methods such as simulation. The study presents a new technique for solving discrete stochastic optimization problems that is similar to a previously developed simulated annealing method designed for solving discrete deterministic optimization problems. It will be demonstrated that the proposed method converges with near certainty to the set of global optimal solutions under mild conditions. Numerical results indicate that for the particular example and choice of parameter values used in this study, the simulated annealing algorithms proposed and analyzed appear to exhibit better overall performance than two, similar, previously developed annealing algorithms.

Operations Research, Planning & Information, Research, Management science, Models, Case studies, Mathematical models, Business planning, Information management, Simulated annealing (Mathematics), Games of strategy (Mathematics)

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Optimization of the transient and steady-state behavior of discrete event systems

Article Abstract:

Simulation can be used to optimize stochastic systems. Optimization is achieved by optimizing the behavior of continuous decision variables. Algorithms improving on the Robbins-Monro stochastic approximation algorithm are used to optimize transient and steady-state discrete event system behavior. The procedures stemming from the approach can be used to optimize the performance of several types of discrete event systems, such as manufacturing systems, computer systems and communications systems. However, the implementation and efficiency of such procedures should be scrutinized in terms of their application to the optimization of particular classes of discrete event systems. Their unique structures should also be used to develop conditions ensuring validity and ease of verification.

Methods, Discrete-time systems, Discrete time systems

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