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W. E. Walker, P. Harremoes, J. Rotmans, J. P. Van der Sluis, M. B. A. Van Asselt, P. Janssen and M. P. Krayer von Krauss, “Defining Uncertainty—A Conceptual Basis for Uncertainty Management in Model-Based Decision Support,” Integrated Assessment, Vol. 4, No. 1, 2003, pp. 5-17. doi:10.1076/iaij.4.1.5.16466

has been cited by the following article:

• TITLE: Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning

  AUTHORS: Julian Scott Yeomans, Raha Imanirad

  KEYWORDS: Simulation-Driven Optimization; Modelling-to-Generate-Alternatives; Environmental Decision Making under Uncertainty

  JOURNAL NAME: Applied Mathematics, Vol.3 No.10A, November 1, 2012

  ABSTRACT: Public sector decision-making typically involves complex problems that are riddled with competing performance objecttives and possess design requirements which are difficult to capture at the time that supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable stochastic uncertainty and frequently numerous stakeholders exist that hold completely incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the problem formulation, which can greatly impact the acceptability of any proposed solutions. While a mathematically optimal solution might provide the best solution to a modelled problem, normally this will not be the best solution to the underlying real problem. Therefore, in public environmental policy formulation, it is generally preferable to be able to create several quantifiably good alternatives that provide very different approaches and perspectives to the problem. This study shows how a computationally efficient simulation-driven optimization approach that com- bines evolutionary optimization with simulation can be used to generate multiple policy alternatives that satisfy required system criteria and are maximally different in decision space. The efficacy of this modelling-to-generate-alternatives method is specifically demonstrated on a municipal solid waste management facility expansion case.