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50 International Journal of Operations Research and Information Systems, 4(2), 50-68, April-June 2013
Generating Alternatives Using Simulation-Optimization Combined with Niching Operators to Address Unmodelled Objectives in a Waste Management Facility Expansion Planning Case Julian Scott Yeomans, Department of Operations Management & Information Systems, York University, Toronto, ON, Canada Yavuz Gunalay, Department of Economic and Administrative Sciences, Bahcesehir University, Istanbul, Turkey
ABSTRACT
Public sector decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult – if not impossible – to quantify and capture when supporting decision models need to be constructed. There are invariably unmodelled design issues, not apparent at the time of model creation, which can greatly impact the acceptability of the solutions proposed by the model. Consequently, it is generally preferable to create several quantifiably good alternatives that provide multiple, disparate perspectives and very different approaches to the particular problem. These alternatives should possess near-optimal objective measures with respect to the known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. By generating a set of very different solutions, it is hoped that some of the dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study shows how simulation-optimization (SO) modelling can be combined with niching operators to efficiently generate multiple policy alternatives that satisfy required system performance criteria in stochastically uncertain environments and yet are maximally different in the decision space. This new stochastic approach is very computationally efficient, since it permits the simultaneous generation of good solution alternatives in a single computational run of the SO algorithm. The efficacy and efficiency of this modelling-to-generate-alternatives (MGA) method is specifically demonstrated on a municipal solid waste management facility expansion case. Keywords:
Environmental Decision Making Under Uncertainty, Modelling to Generate Alternatives (MGA), Niching Operators, Planning, Simulation-Optimization (SO), Strategy
DOI: 10.4018/joris.2013040104 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
International Journal of Operations Research and Information Systems, 4(2), 50-68, April-June 2013 51
INTRODUCTION In public sector decision making, numerous system objectives and requirements always exist that are never explicitly included or apparent during the problem formulation stage. This is a common occurrence in situations where the final decisions must be constructed based not only upon clearly articulated and modelled objectives, but also upon environmental, political and socio-economic goals that are fundamentally subjective (Baugh, Caldwell, & Brill, 1997; Brill, Chang, & Hopkins, 1981; Chandra & Grabis, 2009; Das et al., 2010; Liebman, 1976; Zechman & Ranjithan, 2004). Moreover, it may never be possible to explicitly express many of the subjective considerations in public policy formulation because there are generally numerous competing, adversarial stakeholder groups holding perspectives that are completely incompatible (Baek & Prabhu, 2008). Therefore many of these subjective aspects remain unknown, unquantified and unmodelled in the construction of any corresponding decision models (Chakravorty, Hales, & Herbert, 2008). Thus, in general, public sector decisionmaking typically involves complex problems that are riddled with competing performance objectives and possess design requirements which are very difficult to capture at the time that any supporting decision models are actually constructed (Brugnach et al., 2007; De Kok & Wind, 2003; Hipel & Ben-Haim, 1999; Mowrer, 2000; Papadopoulos & Kanellis, 2011; Walker, Harremoes, Rotmans, Van der Sluis, Van Asselt, Janssen, & Krayer von Krauss, 2003). Environmental policy formulation can prove to be even more complicated because the various system components often also contain considerable stochastic uncertainty (Yeomans, 2008). Consequently, public sector environmental policy formulation proves to be an extremely complicated and challenging undertaking (Janssen, Krol, Schielen, & Hoekstra, 2010; Loughlin, Ranjithan, Brill, & Baugh, 2001). Numerous ancillary modelling approaches have been proposed to support the policy formulation endeavour (Linton, Yeomans, &
Yoogalingam, 2002; Rubenstein-Montano, Anandalingam, & Zandi, 2000) and for environmental policy determination, various deterministic mathematical programming techniques have been introduced (see, for example: Ferrell & Hizlan, 1997; Hasit & Warner, 1981; Haynes, 1981; Lund, 1990; Lund, Tchobanoglous, Anex, & Lawver, 1994; Marks & Liebman, 1971; Walker, 1976). However, while mathematically optimal solutions may provide the best results for the modelled problems, they are frequently not the best solutions for the underlying real problems due to the unmodelled issues and unquantified objectives not apparent at the time of model construction (Chang, Brill, & Hopkins, 1982a, 1982b; Gidley & Bari, 1986; Janssen et al., 2010; Loughlin et al., 2001). Furthermore, although optimization-based techniques are designed to create single best solutions, the presence of the unmodelled issues coupled with the system uncertainties and opposition from powerful intransigent stakeholders can actually lead to the outright elimination of any single (even an optimal) solution from further consideration (De Kok & Wind, 2003; Matthies, Giupponi, & Ostendorf, 2007; Yeomans, 2008; Zechman & Ranjithan, 2004). In the environmental decision-making realm, there are frequently numerous stakeholder groups holding completely incongruent standpoints, essentially dictating that policymakers must establish a decision framework that can concurrently consider numerous irreconcilable points of view (De Kok & Wind, 2003; Matthies et al., 2007; Yeomans, 2002; 2008). Consequently, from an environmental policy formulation standpoint it is generally preferable to be able to generate several alternatives that provide multiple, disparate perspectives to the problem under consideration (Huang, Baetz, & Patry, 1996; Janssen et al., 2010). Preferably these alternatives should all possess quantifiably good (i.e. near-optimal) objective measures with respect to the modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables (Gunalay & Yeomans, 2011). By generating this set of very different
Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
17 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/generating-alternatives-usingsimulation-optimization/78342?camid=4v1
This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Business, Administration, and Management. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2
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www.igi-global.com/chapter/evaluating-adequacy-business-processmodeling/36566?camid=4v1a Piecewise Linear Virtual Inputs/Outputs in Interval DEA Yiannis G. Smirlis and Dimitris K. Despotis (2013). International Journal of Operations Research and Information Systems (pp. 36-49).
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Piecewise Linear Virtual Inputs/Outputs in Interval DEA Yiannis G. Smirlis and Dimitris K. Despotis (2013). International Journal of Operations Research and Information Systems (pp. 36-49).
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Generating Alternatives Using Simulation-Optimization Combined with Niching Operators to Address Unmodelled Objectives in a Waste Management Facility Expansion Planning Case Julian Scott Yeomans, Department of Operations Management & Information Systems, York University, Toronto, ON, Canada Yavuz Gunalay, Department of Economic and Administrative Sciences, Bahcesehir University, Istanbul, Turkey
ABSTRACT
Public sector decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult – if not impossible – to quantify and capture when supporting decision models need to be constructed. There are invariably unmodelled design issues, not apparent at the time of model creation, which can greatly impact the acceptability of the solutions proposed by the model. Consequently, it is generally preferable to create several quantifiably good alternatives that provide multiple, disparate perspectives and very different approaches to the particular problem. These alternatives should possess near-optimal objective measures with respect to the known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. By generating a set of very different solutions, it is hoped that some of the dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study shows how simulation-optimization (SO) modelling can be combined with niching operators to efficiently generate multiple policy alternatives that satisfy required system performance criteria in stochastically uncertain environments and yet are maximally different in the decision space. This new stochastic approach is very computationally efficient, since it permits the simultaneous generation of good solution alternatives in a single computational run of the SO algorithm. The efficacy and efficiency of this modelling-to-generate-alternatives (MGA) method is specifically demonstrated on a municipal solid waste management facility expansion case. Keywords:
Environmental Decision Making Under Uncertainty, Modelling to Generate Alternatives (MGA), Niching Operators, Planning, Simulation-Optimization (SO), Strategy
DOI: 10.4018/joris.2013040104 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
International Journal of Operations Research and Information Systems, 4(2), 50-68, April-June 2013 51
INTRODUCTION In public sector decision making, numerous system objectives and requirements always exist that are never explicitly included or apparent during the problem formulation stage. This is a common occurrence in situations where the final decisions must be constructed based not only upon clearly articulated and modelled objectives, but also upon environmental, political and socio-economic goals that are fundamentally subjective (Baugh, Caldwell, & Brill, 1997; Brill, Chang, & Hopkins, 1981; Chandra & Grabis, 2009; Das et al., 2010; Liebman, 1976; Zechman & Ranjithan, 2004). Moreover, it may never be possible to explicitly express many of the subjective considerations in public policy formulation because there are generally numerous competing, adversarial stakeholder groups holding perspectives that are completely incompatible (Baek & Prabhu, 2008). Therefore many of these subjective aspects remain unknown, unquantified and unmodelled in the construction of any corresponding decision models (Chakravorty, Hales, & Herbert, 2008). Thus, in general, public sector decisionmaking typically involves complex problems that are riddled with competing performance objectives and possess design requirements which are very difficult to capture at the time that any supporting decision models are actually constructed (Brugnach et al., 2007; De Kok & Wind, 2003; Hipel & Ben-Haim, 1999; Mowrer, 2000; Papadopoulos & Kanellis, 2011; Walker, Harremoes, Rotmans, Van der Sluis, Van Asselt, Janssen, & Krayer von Krauss, 2003). Environmental policy formulation can prove to be even more complicated because the various system components often also contain considerable stochastic uncertainty (Yeomans, 2008). Consequently, public sector environmental policy formulation proves to be an extremely complicated and challenging undertaking (Janssen, Krol, Schielen, & Hoekstra, 2010; Loughlin, Ranjithan, Brill, & Baugh, 2001). Numerous ancillary modelling approaches have been proposed to support the policy formulation endeavour (Linton, Yeomans, &
Yoogalingam, 2002; Rubenstein-Montano, Anandalingam, & Zandi, 2000) and for environmental policy determination, various deterministic mathematical programming techniques have been introduced (see, for example: Ferrell & Hizlan, 1997; Hasit & Warner, 1981; Haynes, 1981; Lund, 1990; Lund, Tchobanoglous, Anex, & Lawver, 1994; Marks & Liebman, 1971; Walker, 1976). However, while mathematically optimal solutions may provide the best results for the modelled problems, they are frequently not the best solutions for the underlying real problems due to the unmodelled issues and unquantified objectives not apparent at the time of model construction (Chang, Brill, & Hopkins, 1982a, 1982b; Gidley & Bari, 1986; Janssen et al., 2010; Loughlin et al., 2001). Furthermore, although optimization-based techniques are designed to create single best solutions, the presence of the unmodelled issues coupled with the system uncertainties and opposition from powerful intransigent stakeholders can actually lead to the outright elimination of any single (even an optimal) solution from further consideration (De Kok & Wind, 2003; Matthies, Giupponi, & Ostendorf, 2007; Yeomans, 2008; Zechman & Ranjithan, 2004). In the environmental decision-making realm, there are frequently numerous stakeholder groups holding completely incongruent standpoints, essentially dictating that policymakers must establish a decision framework that can concurrently consider numerous irreconcilable points of view (De Kok & Wind, 2003; Matthies et al., 2007; Yeomans, 2002; 2008). Consequently, from an environmental policy formulation standpoint it is generally preferable to be able to generate several alternatives that provide multiple, disparate perspectives to the problem under consideration (Huang, Baetz, & Patry, 1996; Janssen et al., 2010). Preferably these alternatives should all possess quantifiably good (i.e. near-optimal) objective measures with respect to the modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables (Gunalay & Yeomans, 2011). By generating this set of very different
Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
17 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/generating-alternatives-usingsimulation-optimization/78342?camid=4v1
This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Business, Administration, and Management. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2
Related Content Evaluating Adequacy of Business Process Modeling Approaches Ilia Bider and Erik Perjons (2010). Handbook of Research on Complex Dynamic Process Management: Techniques for Adaptability in Turbulent Environments (pp. 79-102).
www.igi-global.com/chapter/evaluating-adequacy-business-processmodeling/36566?camid=4v1a Piecewise Linear Virtual Inputs/Outputs in Interval DEA Yiannis G. Smirlis and Dimitris K. Despotis (2013). International Journal of Operations Research and Information Systems (pp. 36-49).
www.igi-global.com/article/piecewise-linear-virtual-inputsoutputs/78341?camid=4v1a E-Business Process Management and Intellectual Property: Issues and Implications Kathleen Mykytyn and Peter Mykytyn (2007). E-Business Process Management: Technologies and Solutions (pp. 45-67).
www.igi-global.com/chapter/business-process-management-intellectualproperty/8709?camid=4v1a
Piecewise Linear Virtual Inputs/Outputs in Interval DEA Yiannis G. Smirlis and Dimitris K. Despotis (2013). International Journal of Operations Research and Information Systems (pp. 36-49).
www.igi-global.com/article/piecewise-linear-virtual-inputsoutputs/78341?camid=4v1a
