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Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives

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Simulation-Driven Modeling and Optimization

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 153))

Abstract

In solving municipal solid waste (MSW) planning problems, it is generally preferable to formulate several quantifiably good alternatives that provide multiple, disparate perspectives. This is because MSW 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 when supporting decision models must be constructed. By generating a set of maximally 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 maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). Furthermore, many MSW decision-making problems contain considerable elements of stochastic uncertainty. This chapter provides a firefly algorithm-driven simulation-optimization approach for MGA that can efficiently create multiple solution alternatives to problems containing significant stochastic uncertainties that satisfy required system performance criteria and yet are maximally different in their decision spaces. It is shown that this new computationally efficient algorithmic approach can simultaneously produce the desired number of maximally different solution alternatives in a single computational run of the procedure. The efficacy of this stochastic MGA approach for “real world,” environmental policy formulation is demonstrated using an MSW case study.

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Author information

Authors and Affiliations

  1. Technology and Operations Management, Harvard Business School, Boston, MA, 02163, USA

    Raha Imanirad

  2. Department of Design Engineering and Mathematics, School of Science and Technology, Middlesex University, Hendon Campus, London, NW4 4BT, UK

    Xin-She Yang

  3. OMIS Area, Schulich School of Business, York University, Toronto, ON, Canada M3J 1P3

    Julian Scott Yeomans

Authors
  1. Raha Imanirad
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  2. Xin-She Yang
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  3. Julian Scott Yeomans
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Correspondence to Julian Scott Yeomans .

Editor information

Editors and Affiliations

  1. Engin. Optimization & Modeling Cent, Reykjavik University, Reykjavik, Iceland

    Slawomir Koziel

  2. College of Engineering, Iowa State University, Ames, Iowa, USA

    Leifur Leifsson

  3. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

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Imanirad, R., Yang, XS., Yeomans, J.S. (2016). Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives. In: Koziel, S., Leifsson, L., Yang, XS. (eds) Simulation-Driven Modeling and Optimization. Springer Proceedings in Mathematics & Statistics, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-319-27517-8_12

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