AM  Vol.3 No.10 A , October 2012
Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning
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.

Cite this paper
J. Yeomans and R. Imanirad, "Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning," Applied Mathematics, Vol. 3 No. 10, 2012, pp. 1236-1244. doi: 10.4236/am.2012.330179.
References
[1]   M. Brugnach, A. Tagg, F. Keil and W. J. De Lange, “Uncertainty Matters: Computer Models at the Science-Policy Interface,” Water Resources Management, Vol. 21, No. 7, 2007, pp. 1075-1090. doi:10.1007/s11269-006-9099-y

[2]   J. A. E. B. Janssen, M. S. Krol, R. M. J. Schielen and A. Y. Hoekstra, “The Effect of Modelling Quantified Expert Knowledge and Uncertainty Information on Model Based Decision Making,” Environmental Science and Policy, Vol.13, No. 3, 2010, pp. 229-238. doi:10.1016/j.envsci.2010.03.003

[3]   H. T. Mowrer, “Uncertainty in Natural Resource Decision Support Systems: Sources, Interpretation and Importance,” Computers and Electronics in Agriculture, Vol. 27, No. 1-3, 2000, pp. 139-154. doi:10.1016/S0168-1699(00)00113-7

[4]   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

[5]   D. H. Loughlin, S. R. Ranjithan, E. D. Brill and J. W. Baugh, “Genetic Algorithm Approaches for Addressing Unmodeled Objectives in Optimization Problems,” Engineering Optimization, Vol. 33, No. 5, 2001, pp. 549-569. doi:10.1080/03052150108940933

[6]   M. Matthies, C. Giupponi and B. Ostendorf. “Environmental Decision Support Systems: Current Issues, Methods and Tools,” Environmental Modelling and Software, Vol. 22, No. 2, 2007, pp. 123-127. doi:10.1016/j.envsoft.2005.09.005

[7]   J. S. Yeomans, “Applications of Simulation-Optimization Methods in Environmental Policy Planning UNDER Uncertainty,” Journal of Environmental Informatics, Vol. 12, No. 2, 2008, pp. 174-186. doi:10.3808/jei.200800135

[8]   B. W. Baetz, E. I. Pas and A. W. Neebe, “Generating Alternative Solutions for Dynamic Programming-Based Planning Problems,” Socio-Economic Planning Sciences, Vol. 24, No. 1, 1990, pp. 27-34. doi:10.1016/0038-0121(90)90025-3

[9]   J. W. Baugh, S. C. Caldwell and E. D. Brill, “A Mathematical Programming Approach for Generating Alternatives in Discrete Structural Optimization,” Engineering Optimization, Vol. 28, No. 1, 1997, pp. 1-31. doi:10.1080/03052159708941125

[10]   E. D. Brill, S. Y. Chang and L. D. Hopkins, “Modelling to Generate Alternatives: The HSJ Approach and an Illustration Using a Problem in Land Use Planning,” Management Science, Vol. 27, No. 3, 1981, pp. 314-325. doi:10.1287/mnsc.28.3.221

[11]   J. S. Yeomans and Y. Gunalay, “Simulation-Optimization Techniques for Modelling to Generate Alternatives in Waste Management Planning,” Journal of Applied Operational Research, Vol. 3, No. 1, 2011, pp. 23-35.

[12]   E. M. Zechman and S. R. Ranjithan, “An Evolutionary Algorithm to Generate Alternatives (EAGA) for Engineering Optimization Problems,” Engineering Optimization, Vol. 36, No. 5, 2004, pp. 539-553. doi:10.1080/03052150410001704863

[13]   J. S. Yeomans, G. H. Huang and R. Yoogalingam, “Combining Simulation with Evolutionary Algorithms for Optimal Planning Under Uncertainty: An Application to Municipal Solid Waste Management Planning in the Regional Municipality of Hamilton-Wentworth,” Journal of Environmental Informatics, Vol. 2, No. 1, 2003, pp. 1130. doi:10.3808/jei.200300014

[14]   M. C. Fu, “Optimization via Simulation: A Review,” Annals of Operational Research, Vol. 53, No. 3, 2002, pp. 199-248. doi:10.1287/ijoc.14.3.192.113

[15]   J. D. Linton, J. S. Yeomans and R. Yoogalingam, “Policy Planning Using Genetic Algorithms Combined with Simulation: The Case of Municipal Solid Waste,” Environment and Planning B: Planning and Design, Vol. 29, No. 5, 2002, pp. 757-778. doi:10.1068/b12862

[16]   G. H. Huang, Y. Gunalay and J. S. Yeomans, “Modelling to Generate Alternative Policies in Highly Uncertain Environments: An Application to Municipal Solid Waste Management Planning,” Journal of Environmental Informatics, 2012 (in press)

 
 
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