برنامه ریزی مواد زائد جامد تحت عدم قطعیت با استفاده از بهینه سازی شبیه سازی تکاملی

A key aspect of effective public planning design is to minimize the impact of negative outcomes that can arise from the violation of pre-established system constraint criteria. These planning situations can be especially complicated when several components within the studied system are either unknown or contain considerable stochastic uncertainty. In this paper, the concept of outcome minimization through the use of penalty functions is combined with grey programming (GP) into an evolutionary simulation-optimization (ESO) procedure in order to solve solid waste management problems containing significant sources of uncertainty. By employing outcome minimization concurrently with GP and ESO, it can be shown that plans that meet, or come close to meeting, required system criteria can be efficiently created. The efficacy of the procedure is demonstrated through its application to a solid waste planning case from the Municipality of Hamilton–Wentworth in the Province of Ontario, Canada. Since ESO techniques can be adapted to a wide variety of problem types in which some or all of the system components are stochastic, the practicality of this approach can be adapted to many operational and strategic planning situations containing significant sources of uncertainty

In public policy formulation, planners must balance and integrate many disparate factors prior to settling upon a final decision. To facilitate this process, various ancillary mechanisms for improving decision-making have assumed more prevalent roles, with the field of mathematical programming supplying several of these procedures. These planning models have been used in numerous applications to successfully facilitate the movement toward stated objectives in policy formulation [1] and [2]. It should be recognized, however, that any plan formulated by modelling methods would generally not be operationalized without supplementary decision-maker input [3], since supporting techniques applied without additional expert oversight are unlikely to produce policies that can simultaneously satisfy all conflicting dimensions [4] and [5]. The field of municipal solid waste (MSW) management affords a rich, illustrative environment of the disparate modelling techniques used to support policy formulation, since it possesses many of the conflicting characteristics generally encountered in public planning. Haynes [6] and Wenger and Cruz [7] reviewed several optimization techniques that have been applied to MSW planning problems and additional optimization examples have been considered in [8], [9], [10], [11] and [12]. However, optimization techniques prove appropriate only for well-structured problems [13], [14] and [15] and the numerous uncertain components prevalent within MSW systems render many optimization techniques unsuitable for practical implementation purposes [16], [17] and [18]. Their major drawback is attributable to the fact that they have all been based upon deterministic methods and, therefore, provide no effective mechanism with which to incorporate system uncertainties directly into their solution construction.

MSW systems provide an ideal background for testing a wide variety of modelling techniques used in public policy formulation, since they possess many prevalent incongruencies and uncertainties that often exist in these complex systems. To counteract the many difficulties associated with data uncertainty and deterministic optimization, Huang et al. [22] and [40] had earlier applied both GP and ESO to an MSW planning problem in the municipality of Hamilton–Wentworth. In this paper, the efficacy of simultaneously integrating GP and penalty functions into an ESO procedure was considered using the MSW case data from Hamilton–Wentworth. The testing of this new hybrid approach provided solutions that showed how the municipality could reduce its tax-dependent costs and how these improved solutions could be found significantly faster than in the earlier ESO implementations [40].