Automatic generation of efficient policy alternatives via simulation-optimization
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Automatic generation of efficient policy alternatives via simulation-optimization JS Yeomans* Management Science Area, Schulich School of Business, York University, Toronto, Canada Simulation-optimization methods can be used for many practical and industrial problems in which some or all of the system components are stochastic. These techniques can be applied to a wide variety of problem types, including those in which some functions cannot be represented analytically. In contrast to earlier function optimization approaches, in this paper, these techniques are used for generating several new policy options for planning applications. By using this approach, multiple policy alternatives can be created that meet established system criteria, while simultaneously remaining acceptable and implementable in practice. A subsequent comparative evaluation of the alternatives would be undertaken prior to final policy selection. An illustrative application of the method is provided to demonstrate the usefulness of this approach in the planning phase of policy design. Journal of the Operational Research Society (2002) 53, 1256–1267. doi:10.1057=palgrave.jors.2601439 Keywords: decision-making; stochastic; simulation; optimization; evolutionary algorithms; planning
Introduction Public policy problems generally consist of large complex systems possessing many social, economical, technological, environmental, and political dimensions. These problems become further compounded by multiple conflicting objectives proffered by multiple stakeholders. Because of the incompatibility of these dimensions, policy problems tend to be more complex than many other problem types that have been successfully addressed via OR modelling.1 The inability to validate traditional OR models provides one major reason for their limited success in the implementation of policy problems.2 Modelling approaches, alone, are unlikely to have the capability for automatically determining a single best policy solution that can address all of the multiple conflicting dimensions identified above. Nevertheless, effective planning models can be used to facilitate the movement towards the realization of the objectives set by the policy makers,3,4 if it is recognized that the modelling methods used in policy setting cannot operate without direct decision maker input and expertise. As with any policy formulation problem, planners must balance and integrate many factors prior to establishing a final policy. Notwithstanding the lack of consensus regarding the best planning approach to pursue, Openshaw and Whitehead5 and Harris6 have established the need for a formalized approach to planning, particularly for the effective design and evaluation of policies.
*Correspondence: JS Yeomans, Management Science Area, Schulich School of Business, York University, Toronto, ON, M3J 1P3, Canada. E-mail: [email protected]
In a recent article, Rubenstein-Montano et al1 i
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