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Water Resources Management Decision-Making Under Stochastic Uncertainty Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives

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Intelligence Systems in Environmental Management: Theory and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 113))

Abstract

In solving complex water resources management (WRM) problems, it can prove preferable to create numerous quantifiably good alternatives that provide multiple, disparate perspectives. This is because WRM normally involves complex problems that are riddled with irreconcilable performance objectives and possess contradictory design requirements which are very difficult to quantify and capture when supporting decisions must be constructed. By producing a set of options that are maximally different from each other in terms of their decision variable structures, it is hoped that some of these dissimilar solutions may convey very different perspectives that may serve to address these unmodelled objectives. In environmental planning, this maximally different option production procedure is referred to as modelling-to-generate-alternatives (MGA). Furthermore, many WRM decision-making problems contain considerable elements of stochastic uncertainty. This chapter provides a firefly algorithm-driven simulation-optimization approach for MGA that can be used to 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. This algorithmic approach is both computationally efficient and simultaneously produces a prescribed number of maximally different solution alternatives in a single computational run of the procedure. The effectiveness of this stochastic MGA approach for creating alternatives in “real world”, environmental policy formulation is demonstrated using a WRM case study.

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References

  • Baugh, J. W., Caldwell, S. C., & Brill, E. D. (1997). A mathematical programming approach for generating alternatives in discrete structural optimization. Engineering Optimization, 28(1), 1–31.

    Article  Google Scholar 

  • Brill, E. D., Chang, S. Y., & Hopkins, L. D. (1982). Modelling to generate alternatives: The HSJ approach and an illustration using a problem in land use planning. Management Science, 28(3), 221–235.

    Article  Google Scholar 

  • Brugnach, M., Tagg, A., Keil, F., & De Lange, W. J. (2007). Uncertainty matters: Computer models at the science-policy interface. Water Resources Management, 21, 1075–1090.

    Article  Google Scholar 

  • Cagnina, L. C., Esquivel, C. A., & Coello, C. A. (2008). Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica, 32, 319–326.

    MATH  Google Scholar 

  • Caicedo, J. M., & Yun, G. J. (2011). A novel evolutionary algorithm for identifying multiple alternative solutions in model updating. Structural Health Monitoring—An International Journal, 10(5), 491–501.

    Article  Google Scholar 

  • Caicedo, J. M., & Zarate, B. A. (2011). Reducing epistemic uncertainty using a model updating cognitive system. Advances in Structural Engineering, 14(1), 55–65.

    Article  Google Scholar 

  • Castelletti, A., Galelli, S., Restelli, M., & Soncini-Sessa, R. (2012). Data-driven dynamic emulation modelling for the optimal management of environmental systems. Environmental Modelling and Software, 34(3), 30–43.

    Article  Google Scholar 

  • De Kok, J. L., & Wind, H. G. (2003). Design and application of decision support systems for integrated water management; lessons to be learnt. Physics and Chemistry of the Earth, 28(14–15), 571–578.

    Article  Google Scholar 

  • DeCaroli, J. F. (2011). Using modeling to generate alternatives (MGA) to expand our thinking on energy futures. Energy Economics, 33(2), 145–152.

    Article  Google Scholar 

  • Fu, M. C. (2002). Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing, 14(3), 192–215.

    Article  MathSciNet  MATH  Google Scholar 

  • Fuerst, C., Volk, M., & Makeschin, F. (2010). Squaring the circle? Combining models, indicators, experts and end-users in integrated land-use management support tools. Environmental Management, 46(6), 829–833.

    Article  Google Scholar 

  • Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2011). Mixed variable structural optimization using firefly algorithm. Computers & Structures, 89(23–24), 2325–2336.

    Article  Google Scholar 

  • Gunalay, Y., Yeomans, J. S., & Huang, G. H. (2012). Modelling to generate alternative policies in highly uncertain environments: An application to municipal solid waste management planning. Journal of Environmental Informatics, 19(2), 58–69.

    Google Scholar 

  • Hamalainen, R. P., Luoma, J., & Saarinen, E. (2013). On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems. European Journal of Operational Research, 228(3), 623–634.

    Article  MathSciNet  MATH  Google Scholar 

  • He, L., Huang, G. H., & Zeng, G.-M. (2009). Identifying optimal regional solid waste management strategies through an inexact integer programming model containing infinite objectives and constraints. Waste Management, 29(1), 21–31.

    Article  Google Scholar 

  • Hipel, K. W., & Walker, S. G. B. (2011). Conflict analysis in environmental management. Environmetrics, 22(3), 279–293.

    Article  MathSciNet  Google Scholar 

  • Huang, G. H., & Loucks, D. P. (2000). An inexact two-stage stochastic programming model for water resources management under uncertainty. Civil Engineering and Environmental Systems, 17(1), 95–118.

    Article  Google Scholar 

  • Imanirad, R., Yang, X. S., & Yeomans, J. S. (2012a). A computationally efficient, biologically-inspired modelling-to-generate-alternatives method. Journal on Computing, 2(2), 43–47.

    Google Scholar 

  • Imanirad, R., Yang, X. S., & Yeomans, J. S. (2012b). A co-evolutionary, nature-inspired algorithm for the concurrent generation of alternatives. Journal on Computing, 2(3), 101–106.

    Google Scholar 

  • Imanirad, R., Yang, X. S., & Yeomans, J. S. (2013a). A biologically-inspired metaheuristic procedure for modelling-to-generate-alternatives. International Journal of Engineering Research and Applications, 3(2), 1677–1686.

    Google Scholar 

  • Imanirad, R., Yang, X. S., & Yeomans, J. S. (2013b). Modelling-to-generate-alternatives via the firefly algorithm. Journal of Applied Operational Research, 5(1), 14–21.

    Google Scholar 

  • Imanirad, R., Yang, X. S., & Yeomans, J. S. (2016). Stochastic decision-making in waste management using a firefly algorithm-driven simulation-optimization approach for generating alternatives. In S. Koziel, L. Leifsson, & X. S. Yang (Eds.), Recent advances in simulation-driven modeling and optimization (pp. 299–323). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Imanirad, R., Yang, X. S., & Yeomans, J. S. (2017). Environmental decision-making under uncertainty using a biologically-inspired simulation-optimization algorithm for generating alternative perspectives. International Journal of Business Innovation and Research, 11(1), 38–59.

    Article  Google Scholar 

  • Janssen, J. A. E. B., Krol, M. S., Schielen, R. M. J., & Hoekstra, A. Y. (2010). The effect of modelling quantified expert knowledge and uncertainty information on model based decision making. Environmental Science & Policy, 13(3), 229–238.

    Article  Google Scholar 

  • Kasprzyk, J. R., Reed, P. M., & Characklis, G. W. (2012). Many-objective De Novo water supply Portfolio planning under deep uncertainty. Environmental Modelling and Software, 34, 87–104.

    Article  Google Scholar 

  • Kassab, M., Hipel, K. W., & Hegazy, T. (2011). Multi-criteria decision analysis for infrastructure privatisation using conflict resolution. Structure And Infrastructure Engineering, 7(9), 661–671.

    Article  Google Scholar 

  • Kelly, P. (2002). Simulation optimization is evolving. INFORMS Journal on Computing, 14(3), 223–225.

    Article  Google Scholar 

  • Linton, J. D., Yeomans, J. S., & Yoogalingam, R. (2002). Policy planning using genetic algorithms combined with simulation: The case of municipal solid waste. Environment and Planning B: Planning and Design, 29(5), 757–778.

    Article  Google Scholar 

  • Liu, J. D., Li, Y. P., Huang, G. H., & Zeng, H. T. (2014). A dual-interval fixed-mix stochastic programming method for water resources management under uncertainty. Resources, Conservation and Recycling, 88(1), 50–66.

    Article  Google Scholar 

  • Loughlin, D. H., Ranjithan, S. R., Brill, E. D., & Baugh, J. W. (2001). Genetic algorithm approaches for addressing unmodeled objectives in optimization problems. Engineering Optimization, 33(5), 549–569.

    Article  Google Scholar 

  • Lund, J. (2012). Provoking more productive discussion of wicked problems. Journal of Water Resources Planning and Management, 138(3), 193–195.

    Article  MathSciNet  Google Scholar 

  • Lund, J. R., Tchobanoglous, G., Anex, R. P., & Lawver, R. A. (1994). Linear programming for analysis of material recovery facilities. ASCE Journal of Environmental Engineering, 120, 1082–1094.

    Article  Google Scholar 

  • Maqsood, I. M., Huang, G. H., & Yeomans, J. S. (2005). Water resources management under uncertainty: An interval-parameter fuzzy two-stage stochastic programming approach. European Journal of Operational Research, 167(1), 208–225.

    Article  MathSciNet  MATH  Google Scholar 

  • Martinez, L. J., Joshi, N. N., & Lambert, J. H. (2011). Diagramming qualitative goals for multiobjective project selection in large-scale systems. Systems Engineering, 14(1), 73–86.

    Article  Google Scholar 

  • Matthies, M., Giupponi, C., & Ostendorf, B. (2007). Environmental decision support systems: Current issues, methods and tools. Environmental Modelling and Software, 22(2), 123–127.

    Article  Google Scholar 

  • McIntosh, B. S., Ascough, J. C., & Twery, M. (2011). Environmental decision support systems (EDSS) development—Challenges and best practices. Environmental Modelling and Software, 26(12), 1389–1402.

    Article  Google Scholar 

  • Mowrer, H. T. (2000). Uncertainty in natural resource decision support systems: Sources, interpretation and importance. Computers and Electronics in Agriculture, 27(1–3), 139–154.

    Article  Google Scholar 

  • Reed, P. M., & Kasprzyk, J. R. (2009). Water resources management: The myth, the wicked, and the future. Journal of Water Resources Planning and Management, 135(6), 411–413.

    Article  Google Scholar 

  • Rubenstein-Montano, B., & Zandi, I. (1999). Application of a genetic algorithm to policy planning: The case of solid waste. Environment and Planning B: Planning and Design, 26(6), 791–907.

    Article  Google Scholar 

  • Rubenstein-Montano, B., Anandalingam, G., & Zandi, I. (2000). A genetic algorithm approach to policy design for consequence minimization. European Journal of Operational Research, 124, 43–54.

    Article  MATH  Google Scholar 

  • Sowell, T. (1987). A conflict of visions. New York: William Morrow & Co.

    Google Scholar 

  • Sun, W., & Huang, G. H. (2010). Inexact piecewise quadratic programming for waste flow allocation under uncertainty and nonlinearity. Journal Of Environmental Informatics, 16(2), 80–93.

    Article  Google Scholar 

  • Tchobanoglous, G., Thiesen, H., & Vigil, S. (1993). Integrated solid waste management: Engineering principles and management issues. New York: McGraw-Hill.

    Google Scholar 

  • Thekdi, S. A., & Lambert, J. H. (2012). Decision analysis and risk models for land development affecting infrastructure systems. Risk Analysis, 32(7), 1253–1269.

    Article  Google Scholar 

  • Trutnevyte, E., Stauffacher, M., & Schlegel, M. (2012). Context-specific energy strategies: Coupling energy system visions with feasible implementation scenarios. Environmental Science and Technology, 46(17), 9240–9248.

    Article  Google Scholar 

  • Ursem, R. K., & Justesen, P. D. (2012). Multi-objective distinct candidates optimization: Locating a few highly different solutions in a circuit component sizing problem. Applied Soft Computing, 12(1), 255–265.

    Article  Google Scholar 

  • van Delden, H., Seppelt, R., White, R., & Jakeman, A. J. (2012). A methodology for the design and development of integrated models for policy support. Environmental Modelling and Software, 26(3), 266–279.

    Article  Google Scholar 

  • Walker, W. E., Harremoes, P., Rotmans, J., Van der Sluis, J. P., Van Asselt, M. B. A., Janssen, P., et al. (2003). Defining uncertaint—A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4(1), 5–17.

    Article  Google Scholar 

  • Walker, S. G. B., Hipel, K. W., & Inohara, T. (2012). Attitudes and preferences: Approaches to representing decision maker desires. Applied Mathematics and Computation, 218(12), 6637–6647.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, L., Fang, L., & Hipel, K. W. (2007). On achieving fairness in the allocation of scarce resources: Measurable principles and multiple objective optimization approaches. IEEE Systems Journal, 1(1), 17–28.

    Article  Google Scholar 

  • Wang, S., & Huang, G. H. (2015a). A multi-level Taguchi-factorial two-stage stochastic programming approach for characterization of parameter uncertainties and their interactions: An application to water resources management. European Journal of Operational Research, 240(2), 572–581.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, S., & Huang, G. H. (2015b). An integrated approach for water resources decision making under interactive and compound uncertainties. Omega, 44(1), 32–40.

    Google Scholar 

  • Yang, X. S. (2009). Firefly algorithms for multimodal optimization. Lecture Notes in Computer Science, 5792, 169–178.

    Article  MathSciNet  MATH  Google Scholar 

  • Yang, X. S. (2010). Nature-inspired metaheuristic algorithms (2nd ed.). Frome, UK: Luniver Press.

    Google Scholar 

  • Yeomans, J. S. (2002). Automatic generation of efficient policy alternatives via simulation-optimization. Journal of the Operational Research Society, 53(11), 1256–1267.

    Article  MATH  Google Scholar 

  • Yeomans, J. S. (2008). Applications of simulation-optimization methods in environmental policy planning under uncertainty. Journal of Environmental Informatics, 12(2), 174–186.

    Article  Google Scholar 

  • Yeomans, J. S. (2010). Applications of information technology techniques for water resources planning under uncertainty. International Journal of Technology, Knowledge and Society, 6(2), 57–66.

    Article  Google Scholar 

  • Yeomans, J. S. (2011). Efficient generation of alternative perspectives in public environmental policy formulation: Applying co-evolutionary simulation-optimization to municipal solid waste management. Central European Journal of Operations Research, 19(4), 391–413.

    Article  Google Scholar 

  • Yeomans, J. S. (2012). Waste management facility expansion planning using simulation-optimization with grey programming and penalty functions. International Journal of Environmental and Waste Management, 10(2/3), 269–283.

    Article  Google Scholar 

  • Yeomans, J. S., & Gunalay, Y. (2008a). Water resources policy formulation using simulation optimization combined with fuzzy interval programming. Asian Journal of Information Technology, 7(8), 374–380.

    Google Scholar 

  • Yeomans, J. S., & Gunalay, Y. (2008b). Water resources planning under uncertainty using simulation optimization. Lecture Notes in Management Science, 1, 286–295.

    Google Scholar 

  • Yeomans, J. S., & Gunalay, Y. (2009). Using simulation optimization techniques for water resources planning. Journal of Applied Operational Research, 1(1), 2–14.

    Google Scholar 

  • Yeomans, J. S., & Gunalay, Y. (2011). Simulation-Optimization techniques for modelling to generate alternatives in waste management planning. Journal of Applied Operational Research, 3(1), 23–35.

    Google Scholar 

  • Yeomans, J. S., & Yang, X. S. (2014). Municipal waste management optimization using a firefly algorithm-driven simulation-optimization approach. International Journal of Process Management and Benchmarking, 4(4), 363–375.

    Article  Google Scholar 

  • Yeomans, J. S., Huang, G. H., & Yoogalingam, R. (2003). Combining simulation with evolutionary algorithms for optimal planning under uncertainty: An application to municipal solid waste management planning in the Regional Municipality of Hamilton-Wentworth. Journal of Environmental Informatics, 2(1), 11–30.

    Article  Google Scholar 

  • Zarate, B. A., & Caicedo, J. M. (2008). Finite element model updating: Multiple alternatives. Engineering Structures, 30(12), 3724–3730.

    Article  Google Scholar 

  • Zechman, E. M., & Ranjithan, S. R. (2007). Generating alternatives using evolutionary algorithms for water resources and environmental management problems. Journal of Water Resources Planning and Management, 133(2), 156–165.

    Article  Google Scholar 

  • Zhou, Y., Huang, G. H., & Yang, B. (2013). Water resources management under multi-parameter interactions: A factorial multi-stage stochastic programming approach. Omega, 41(3), 559–573.

    Article  Google Scholar 

  • Zou, R., Liu, Y., Riverson, J., Parker, A., & Carter, S. (2010). A nonlinearity interval mapping scheme for efficient waste allocation simulation-optimization analysis. Water Resources Research, 46(8), 1–14.

    Article  Google Scholar 

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

    Julian Scott Yeomans

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  1. Julian Scott Yeomans
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  1. Fac. Management, Dept of Industrial Engg, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Magt Facu, Department of Industrial Engd, Istanbul Technical University, Istanbul, Turkey

    İrem Uçal Sari

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Yeomans, J.S. (2017). Water Resources Management Decision-Making Under Stochastic Uncertainty Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives. In: Kahraman, C., Sari, İ. (eds) Intelligence Systems in Environmental Management: Theory and Applications. Intelligent Systems Reference Library, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-42993-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-42993-9_10

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