caa a22 4500 44535982X CHVBK 20180317142911.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s10100-011-0190-y doi (NATIONALLICENCE)springer-10.1007/s10100-011-0190-y Yeomans Julian OMIS Area, Schulich School of Business, York University, 4700 Keele Street, M3J 1P3, Toronto, ON, Canada aut Efficient generation of alternative perspectives in public environmental policy formulation: applying co-evolutionary simulation-optimization to municipal solid waste management [Elektronische Daten] [Julian Yeomans] In public policy formulation, it is generally preferable to create several quantifiably good alternatives that provide very different approaches to the particular situation. This is because 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 at the time supporting decision models are constructed. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model's solutions. Furthermore, public environmental policy formulation problems often contain considerable stochastic uncertainty and there are frequently numerous stakeholders with irreconcilable perspectives involved. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the 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 these dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study provides a co-evolutionary simulation-optimization modelling-to-generate-alternatives approach that can be used to efficiently create multiple solution alternatives that satisfy required system performance criteria in highly uncertain environments and yet are maximally different in their 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 technique is specifically demonstrated using an earlier waste management case to enable direct comparisons to previous methods. Waste management systems provide an ideal setting for illustrating the modelling techniques used for such public environmental policy formulation, since they possess all of the prevalent incongruencies and system uncertainties inherent in complex planning processes. Springer-Verlag, 2011 Modelling to generate alternatives nationallicence Simulation-optimization nationallicence Environmental decision making under uncertainty nationallicence Planning and strategy nationallicence Central European Journal of Operations Research Springer-Verlag 19/4(2011-12-01), 391-413 1435-246X 19:4<391 2011 19 10100 https://doi.org/10.1007/s10100-011-0190-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10100-011-0190-y text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Yeomans Julian OMIS Area, Schulich School of Business, York University, 4700 Keele Street, M3J 1P3, Toronto, ON, Canada aut NATIONALLICENCE 773 0- Central European Journal of Operations Research Springer-Verlag 19/4(2011-12-01), 391-413 1435-246X 19:4<391 2011 19 10100 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer