A multi-criteria policy set optimisation framework for large-scale simulation models

摘要

Simulation modelling is an analysis approach utilised in nearly every domain for analysis of large and complex systems. Synchronous data flow (SDF) is used to model systems whose data does not follow the predetermined global schedule of discrete event simulation modelling techniques. A policy set optimisation (PSO) problem for any simulation model is the selection of a small set of controllable inputs for manipulation by a decision maker (DM) in order to achieve a desired goal. This is a multi-criteria decision making problem and a large-scale SDF simulation model creates a complex mathematical model for solution. Our PSO framework and associated procedure aims to generate the policies that will provide an estimation of the Pareto optimal solutions for the simulation model using only pre-processed model sampling. Our solution methodologies for the PSO problem aims to minimise the computation time required from the point at which a DM selects the outcomes of interest, to when they receive solution policies to choose from. This paper provides a sample problem and a discussion about the quality of the solution found.