Applications of Simulation-Optimization Methods in Environmental Policy Planning under Uncertainty

Abstract

ABSTRACT. Environmental policy formulation can prove especially complicated, since in general, system components contain considerable degrees of uncertainty. However, simulation-optimization (SO) techniques can be adapted to model a wide variety of problem types in which system components are stochastic. In this paper, it is shown how multiple environmental policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created using SO. The efficacy of this MGA approach is illustrated using two case studies. Furthermore, since SO techniques can be adapted to problems in which many system components are stochastic, the practicality of this approach can be extended into many other operational and strategic planning applications containing significant sources of uncertainty.

Modelling to generate alternatives in waste management facility expansion planning via simulation-optimization

“…Therefore these subjective aspects remain unquantified and unmodelled in the construction of any corresponding decision models. This is a common occurrence in situations where the final decisions are constructed based not only upon clearly stated and modelled objectives, but also upon environmental, political and socioeconomic goals and stakeholder preferences (Yeomans, 2008;Gunalay et al, 2012).…”

“…While this section provides a brief synopsis of the SO process, more specific details can be found in Yeomans (2008). Determining optimal solutions to large stochastic problems proves to be very complicated when system uncertainties have to be incorporated directly into the solution procedure (Fu 2002).…”

“…The application of the SO MGA procedure will be illustrated using the stochastic municipal waste facilities expansion case study from Yeomans (2008). The region in the expansion planning problem contains three separate municipalities whose MSW disposal needs are collectively met by a landfill and two waste-to-energy (WTE) incinerators.…”

“…In this paper, it is shown how to efficiently generate a set of maximally different solution alternatives for public environmental policy planning situations containing considerable stochastic uncertainty using SO. This study illustrates the efficacy of the MGA capabilities of this new SO procedure to construct very different, good solutions by testing it on the municipal solid waste (MSW) facility expansion case study taken from Yeomans (2008).…”

“…In so doing, the resulting alternative solution set is likely to represent truly different choices that all perform somewhat similarly with respect to the modelled objectives, yet very differently with respect to the unmodelled issues. For function optimization, Yeomans (2008) showed how stochastic uncertainty could be incorporated directly into environmental planning using an approach referred to as simulation-optimization (SO). In this paper, it is shown how to efficiently generate a set of maximally different solution alternatives for public environmental policy planning situations containing considerable stochastic uncertainty using SO.…”