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Policy Planning Using Genetic Algorithms Combined with Simulation: The Case of Municipal Solid Waste

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  • Jonathan D Linton

    (Department of Management, Polytechnic University, 421C Dibner, Six Metrotech Center, Brooklyn, NY 11201, USA)

  • Julian Scott Yeomans
  • Reena Yoogalingam

Abstract

Previous research had introduced a genetic algorithm procedure for creating alternative policy options for municipal solid waste (MSW) management planning. These alternatives were generated during the design phase of planning, with the final policy determined in subsequent comparative analysis. However, because of the many uncertain factors that exist within MSW systems, this earlier procedure cannot be applied to situations containing such stochastic components. In this paper, it is shown that a generic algorithm approach can be simultaneously combined with simulation to incorporate these stochastic elements in the policy option generation phase; thereby permitting uncertainty to be directly integrated into the construction of the alternatives during the planning-design phase. This procedure is applied to case data taken from the Regional Municipality of Hamilton–Wentworth in the Province of Ontario, Canada. It can be shown that this procedure extends the earlier approach and provides many practical planning benefits for problems when uncertain conditions are present.

Suggested Citation

  • Jonathan D Linton & Julian Scott Yeomans & Reena Yoogalingam, 2002. "Policy Planning Using Genetic Algorithms Combined with Simulation: The Case of Municipal Solid Waste," Environment and Planning B, , vol. 29(5), pages 757-778, October.
    • Handle: RePEc:sae:envirb:v:29:y:2002:i:5:p:757-778
    DOI: 10.1068/b12862
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    References listed on IDEAS

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    1. Stein W. Wallace, 2000. "Decision Making Under Uncertainty: Is Sensitivity Analysis of Any Use?," Operations Research, INFORMS, vol. 48(1), pages 20-25, February.
    2. Azadivar, Farhad & Tompkins, George, 1999. "Simulation optimization with qualitative variables and structural model changes: A genetic algorithm approach," European Journal of Operational Research, Elsevier, vol. 113(1), pages 169-182, February.
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