Generating Alternatives Using Simulation-Optimization Combined with Niching Operators to Address Unmodelled Objectives in a Waste Management Facility Expansion Planning Case

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) and Yavuz Gunalay (Department of Economic and Administrative Sciences, Bahcesehir University, Istanbul, Turkey)
DOI: 10.4018/joris.2013040104
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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.
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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 decision-making 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).

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