Using multiple objective optimisation to generate policy insights for system dynamics models.

Abstract

Multiple objective optimisation (MOO) is an optimisation approach that has been widely used to solve optimisation problems with more than one objective function. The benefit of this approach is that it generates a set of non-dominated solutions which a policy maker can explore and evaluate before making a final optimal selection. This paper demonstrates that MOO can be used to assist policy makers explore a richer set of alternatives when deciding on a range of values for key parameters in their system dynamics model. In order to demonstrate the approach, a well-known case study "The Domestic Manufacturing Company" is used, and a stock and flow model and a multiple objective optimiser are designed and coded. The results show that valid solutions are generated, and that each of these solutions can be examined independently - and hence give greater insight into the problem at hand - before a decision is made as to the most appropriate solution.