AN EVOLUTIONARY SIMULATION-OPTIMIZATION APPROACH FOR ADDRESSING UNMODELLED OBJECTIVES IN WASTE MANAGEMENT FACILITY EXPANSION PLANNING

 

Julian Scott Yeomans, Yavuz Gunalay

 

Abstract

 

Public sector decision-making typically involves complex problems that are riddled with competing performance objectives and possess design requirements which are difficult to capture at the time that supporting decision models are constructed. Environ-mental policy formulation can prove additionally complicated because the various system components often contain considerable stochastic uncertainty and frequently there are also numerous stakeholders holding 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 sev-eral quantifiably good alternatives that provide very different approaches and perspectives to the problem. This study shows how simulation-optimization (SO) modelling can be combined with a niching approach 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. The efficacy of this modelling-to-generate-alternatives method is specifically demonstrated on a municipal solid waste management facility expansion case.

 

Lecture Notes in Management Science (2011) Vol. 3: 207-222

3rd International Conference on Applied Operational Research, Proceedings

© Tadbir Operational Research Group Ltd. All rights reserved.

www.tadbir.ca

 

ISSN 2008-0050 (Print)

ISSN 1927-0097 (Online)

 

ARTICLE OUTLINE

 

·         Introduction

·         Simulation-Optimization For Function Optimization

·         Modelling To Generate Policy Alternatives With Simulation- Optimization

·         Case Study Of SO Used In MGA For Municipal Solid Waste Management Planning

·         Conclusions

·         References

 

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