Optimization via multimodel simulation

Simulation Optimization via Metamodeling Approach

Simulation optimization (SO) is the process of finding the best set of input variable values without explicitly evaluating each feasible set of these input variable values given an output criterion (Law, 2007; Fu, 1994). Input variables are called (controllable) inputs, parameter settings, values, variables, (proposed) solutions, designs, configurations, or factors (in design of experiments ter...

Optimization via Simulation: a Review

We review techniques for optimizing stochastic discrete-event systems via simulation. We discuss both the discrete parameter case and the continuous parameter case, but concentrate on the latter which has dominated most of the recent research in the area. For the discrete parameter case, we focus on the techniques for optimization from a finite set: multiple-comparison procedures and ranking-an...

A Multimodel Approach to Reasoning and Simulation

| Models that are constructed within the bounds of a single paradigm are not suucient for modeling all aspects of complex systems. Therefore, even though reasoning and simulation systems that utilize a single modeling paradigm are the current norm, we explore a multimodel approach in this paper. A multimodel approach is deened as one in which more than one model | each derived from a diierent p...

Performance Analysis and Optimization via Simulation

Discrete Optimization via Simulation Using COMPASS

We propose an optimization-via-simulation algorithm, called COMPASS, for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables are integer ordered. We prove that COMPASS converges to the set of local optimal solutions with probability 1 for both terminating and steady-state simulation, and for both fully constrained problems and pa...