Creating Alternatives for Stochastic Water Resources Management Decision-Making Using a Firefly Algorithm-Driven Simulation-Optimization Approach 
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Keywords

Water resources management
Modelling-to-generate alternatives
Firefly Algorithm

How to Cite

1.
Ting Cao, Julian Scott Yeomans. Creating Alternatives for Stochastic Water Resources Management Decision-Making Using a Firefly Algorithm-Driven Simulation-Optimization Approach . Glob. Environ. Eng. [Internet]. 2017 Jan. 30 [cited 2024 Dec. 23];3(2):49-62. Available from: https://avantipublisher.com/index.php/tgevnie/article/view/1012

Abstract

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 multifaceted problems that are riddled with incompatible performance objectives and contain inconsistent 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 unmodelled 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). In addition, many components of WRM problems possess extensive stochastic uncertainty. This study 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.
https://doi.org/10.15377/2410-3624.2016.03.02.2
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References

Huang GH and Loucks DP. An inexact two-stage stochastic programming model for water resources management under uncertainty. Civil Engineering and Environmental Systems 2000; 17(1): 95-118. https://doi.org/10.1080/02630250008970277

Maqsood IM, Huang GH and Yeomans JS. Water resources management under uncertainty: An interval-parameter fuzzy two-stage stochastic programming approach. European Journal of Operational Research 2005; 167(1): 208-225. https://doi.org/10.1016/j.ejor.2003.08.068

Imanirad R, Yang XS and Yeomans JS. Environmental decision-making under uncertainty using a biologicallyinspired simulation-optimization algorithm for generating alternative perspectives. International Journal of Business Innovation and Research. In Press 2014.

Linton JD, Yeomans JS and Yoogalingam R. Policy planning using genetic algorithms combined with simulation: The case of municipal solid waste. Environment and Planning B: Planning and Design 2002; 29(5): 757-778. https://doi.org/10.1068/b12862

Yeomans JS and Yang XS. Municipal waste management optimization using a firefly algorithm-driven simulationoptimization approach. International Journal of Process Management and Benchmarking 2014b; 4(4): 363-375. https://doi.org/10.1504/IJPMB.2014.065518

Brugnach M, Tagg A, Keil F and De Lange WJ. Uncertainty matters: computer models at the science-policy interface. Water Resources Management 2007; 21: 1075-1090. https://doi.org/10.1007/s11269-006-9099-y

Castelletti A, Galelli S, Restelli M and Soncini-Sessa R. Datadriven dynamic emulation modelling for the optimal management of environmental systems. Environmental Modelling and Software 2012; 34(3): 30-43. https://doi.org/10.1016/j.envsoft.2011.09.003

De Kok JL and Wind HG. Design and application of decision support systems for integrated water management; lessons to be learnt. Physics and Chemistry of the Earth 200; 28(14-15): 571-578.

Hipel KW and Walker SGB. Conflict Analysis in Environmental Management. Environmetrics 2011; (3): 279-293. https://doi.org/10.1002/env.1048

Janssen JAEB, Krol MS, Schielen RMJ and Hoekstra AY. The effect of modelling quantified expert knowledge and uncertainty information on model based decision making. Environmental Science and Policy 2010; 13(3): 229-238. https://doi.org/10.1016/j.envsci.2010.03.003

Lund J. Provoking More Productive Discussion of Wicked Problems. Journal of Water Resources Planning and Management 2012; 138(3): 193-195. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000190

Mowrer HT. Uncertainty in natural resource decision support systems: Sources, interpretation, and importance. Computers and Electronics in Agriculture 2000; 27(1-3): 139-154. https://doi.org/10.1016/S0168-1699(00)00113-7

Walker SGB, Hipel KW and Inohara T. Attitudes and preferences: approaches to representing decision maker desires. Applied Mathematics and Computation 2012; 218(12): 6637-6647. https://doi.org/10.1016/j.amc.2011.11.102

Fuerst C, Volk M and Makeschin F. Squaring the circle? Combining models, indicators, experts and end-users in integrated land-use management support tools. Environmental Management 2010; 46(6): 829-833. https://doi.org/10.1007/s00267-010-9574-3

Wang L, Fang L and Hipel KW. On achieving fairness in the allocation of scarce resources: Measurable principles and multiple objective optimization approaches. IEEE Systems Journal 2007; 1(1): 17-28. https://doi.org/10.1109/JSYST.2007.900242

Baugh JW, Caldwell SC and Brill ED. A mathematical programming approach for generating alternatives in discrete structural optimization. Engineering Optimization 1997; 28(1): 1-31. https://doi.org/10.1080/03052159708941125

Brill ED, Chang SY and Hopkins LD. Modelling to generate alternatives: the HSJ approach and an illustration using a problem in land use planning. Management Science 1982; 28(3): 221-235. https://doi.org/10.1287/mnsc.28.3.221

Zechman EM and Ranjithan SR. Generating alternatives using evolutionary algorithms for water resources and environmental management. Problems. Journal of Water Resources Planning and Management 2007; 133(2): 156-165. https://doi.org/10.1061/(ASCE)0733-9496(2007)133:2(156)

Kasprzyk JR, Reed PM and Characklis GW. Many-objective de novo water supply portfolio planning under deep uncertainty. Environmental Modelling & Software 2012; 34: 87-104. https://doi.org/10.1016/j.envsoft.2011.04.003

Yeomans JS. Applications of simulation-optimization methods in environmental policy planning under uncertainty. Journal of Environmental Informatics 2008; 12(2): 174-186. https://doi.org/10.3808/jei.200800135

Loughlin DH, Ranjithan SR, Brill ED and Baugh JW. Genetic algorithm approaches for addressing unmodelled objectives in optimization problems. Engineering Optimization 2001; 33(5): 549-569. https://doi.org/10.1080/03052150108940933

Van Delden H, Seppelt R, White R and Jakeman AJ. A methodology for the design and development of integrated models for policy support. Environmental Modelling & Software 2012; 26(3): 266-279. https://doi.org/10.1016/j.envsoft.2010.03.021

Lund JR, Tchobanoglous G, Anex RP and Lawver RA. Linear programming for analysis of material recovery facilities. ASCE Journal of Environmental Engineering 1994; 120: 1082-1094. https://doi.org/10.1061/(ASCE)0733-9372(1994)120:5(1082)

Rubenstein-Montano B and Zandi I. Application of a genetic algorithm to policy planning: the case of solid waste. Environment and Planning B: Planning and Design 1999; 26(6): 791-907. https://doi.org/10.1068/b260893

Rubenstein-Montano B, Anandalingam G and Zandi I. A genetic algorithm approach to policy design for consequence minimization. European Journal of Operational Research 2000; 124: 43-54. https://doi.org/10.1016/S0377-2217(99)00123-X

Hamalainen RP, Luoma J and Saarinen E. On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems. European Journal of Operational Research 2013; 228(3): 623-634. https://doi.org/10.1016/j.ejor.2013.02.001

Martinez LJ, Joshi NN and Lambert JH. Diagramming qualitative goals for multiobjective project selection in largescale systems. Systems Engineering 2011; 14(1): 73-86. https://doi.org/10.1002/sys.20164

Reed PM and Kasprzyk JR. Water resources management: the myth, the wicked, and the future. Journal of Water Resources Planning and Management 2009; 135(6): 411-413. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000047

Trutnevyte E, Stauffacher M and Schlegel M. Contextspecific energy strategies: coupling energy system visions with feasible implementation scenarios. Environmental Science and Technology 2012; 46(17): 9240-9248. https://doi.org/10.1021/es301249p

Caicedo JM and Zarate BA. Reducing epistemic uncertainty using a model updating cognitive system. Advances in Structural Engineering 2011; 14(1): 55-65. https://doi.org/10.1260/1369-4332.14.1.55

He L, Huang GH and Zeng G-M. Identifying optimal regional solid waste management strategies through an inexact integer programming model containing infinite objectives and constraints. Waste Management 2009; 29(1): 21-31. https://doi.org/10.1016/j.wasman.2008.02.003

Kassab M, Hipel KW and Hegazy T. Multi-criteria decision analysis for infrastructure privatisation using conflict resolution. Structure and Infrastructure Engineering 2011; 7(9): 661-671. https://doi.org/10.1080/15732470802677649

Matthies M, Giupponi C and Ostendorf B. Environmental decision support systems: Current issues, methods and tools. Environmental Modelling and Software 2007; 22(2): 123-127. https://doi.org/10.1016/j.envsoft.2005.09.005

Sowell T. A Conflict of Visions. New York: William Morrow and Co 1987.

McIntosh BS, Ascough JC and Twery M. Environmental decision support systems (EDSS) development - challenges and best practices. Environmental Modelling & Software 2011; 26(12): 1389-1402. https://doi.org/10.1016/j.envsoft.2011.09.009

Yeomans JS. Automatic Generation of efficient policy alternatives via simulation-optimization. Journal of the Operational Research Society 2002; 53(11): 1256-1267. https://doi.org/10.1057/palgrave.jors.2601439

Gunalay Y, Yeomans JS and Huang GH. Modelling to generate alternative policies in highly uncertain environments: An application to municipal solid waste management planning. Journal of Environmental Informatics 2012; 19(2): 58-69.

Yeomans JS. Efficient generation of alternative perspectives in public environmental policy formulation: applying coevolutionary simulation-optimization to municipal solid waste management. Central European Journal of Operations Research 2011; 19(4): 391-413. https://doi.org/10.1007/s10100-011-0190-y

Yeomans JS and Gunalay Y. Simulation-optimization techniques for modelling to generate alternatives in waste management planning. Journal of Applied Operational Research 2011; 3(1): 23-35.

Caicedo JM and Yun GJ. A novel evolutionary algorithm for identifying multiple alternative solutions in model updating. Structural Health Monitoring-An International Journal 2011; 10(5): 491-501. https://doi.org/10.1177/1475921710381775

DeCaroli JF. Using modeling to generate alternatives (MGA) to expand our thinking on energy futures. Energy Economics 2011; 33(2): 145-152. https://doi.org/10.1016/j.eneco.2010.05.002

Ursem RK and Justesen PD. Multi-objective distinct candidates optimization: locating a few highly different solutions in a circuit component sizing problem. Applied Soft Computing 2012; 12(1): 255-265. https://doi.org/10.1016/j.asoc.2011.08.048

Zarate BA and Caicedo JM. Finite element model updating: multiple alternatives. Engineering Structures 2008; 30(12): 3724-3730. https://doi.org/10.1016/j.engstruct.2008.06.012

Sun W and Huang GH. Inexact piecewise quadratic programming for waste flow allocation under uncertainty and nonlinearity. Journal of Environmental Informatics 2010; 16(2): 80-93. https://doi.org/10.3808/jei.201000180

Tchobanoglous G, Thiesen H and Vigil, S. Integrated solid waste management: engineering principles and management issues. New York: McGraw-Hill 1993.

Thekdi SA and Lambert JH. Decision analysis and risk models for land development affecting infrastructure systems. Risk Analysis 2012; 32(7): 1253-1269. https://doi.org/10.1111/j.1539-6924.2011.01719.x

Yeomans JS, Huang GH and Yoogalingam R. 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 2003; 2(1): 11-30. https://doi.org/10.3808/jei.200300014

Fu MC. Optimization for simulation: theory vs. practice. INFORMS Journal on Computing 2002; 14(3): 192-215. https://doi.org/10.1287/ijoc.14.3.192.113

Kelly P. Simulation optimization is evolving. INFORMS Journal on Computing 2002; 14(3): 223-225. https://doi.org/10.1287/ijoc.14.3.223.108

Zou R, Liu Y, Riverson J, Parker A and Carter S. A nonlinearity interval mapping scheme for efficient waste allocation simulation-optimization analysis. Water Resources Research 2010; 46(8): 1-14. https://doi.org/10.1029/2009WR008753

Yang XS. Firefly algorithms for multimodal optimization. Lecture Notes in Computer Science 2009; 5792: 169-178. https://doi.org/10.1007/978-3-642-04944-6_14

Yang XS. Nature-Inspired Metaheuristic Algorithms 2nd Ed. Luniver Press. Frome, UK 2010.

Imanirad R, Yang XS and Yeomans JS. A computationally efficient, biologically-inspired modelling-to-generatealternatives method. Journal on Computing 2012; 2(2): 43-47.

Imanirad R, Yang XS and Yeomans JS. A co-evolutionary, nature-inspired algorithm for the concurrent generation of alternatives. Journal on Computing 2012; 2(3): 101-106.

Imanirad R, Yang XS and Yeomans JS. A biologicallyinspired metaheuristic procedure for modelling-to-generatealternatives. International Journal of Engineering Research and Applications 2013; 3(2): 1677-1686.

Imanirad R, Yang XS and Yeomans JS. Modelling-togenerate- alternatives via the firefly algorithm. Journal of Applied Operational Research 2013; 5(1): 14-21.

Imanirad R, Yang XS and Yeomans JS. Stochastic Decision- Making in Waste Management Using a Firefly Algorithm- Driven Simulation-Optimization Approach for Generating Alternatives. In Recent Advances in Simulation-Driven Modeling and Optimization, S. Koziel, L. Leifsson, X-S. Yang (ed.), Springer, Heidelberg, Germany 2016; pp. 299-323. https://doi.org/10.1007/978-3-319-27517-8_12

Gandomi AH, Yang XS and Alavi AH. Mixed variable structural optimization using firefly algorithm. Computers and Structures 2011; 89(23-24): 2325-2336. https://doi.org/10.1016/j.compstruc.2011.08.002

Yeomans JS. Applications of information technology techniques for water resources planning under uncertainty. International Journal of Technology, Knowledge, and Society 2010; 6(2): 57-66. https://doi.org/10.18848/1832-3669/CGP/v06i02/56091

Yeomans JS and Gunalay Y. Water resources policy formulation using simulation optimization combined with fuzzy interval programming. Asian Journal of Information Technology 2008; 7(8): 374-380.

Yeomans JS and Gunalay Y. Water resources planning under uncertainty using simulation optimization. Lecture Notes in Management Science 2008; 1: 286-295.

Yeomans JS and Gunalay Y. Using simulation optimization techniques for water resources planning. Journal of Applied Operational Research 2009; 1(1): 2-14.

Yeomans JS. Waste management facility expansion planning using simulation-optimization with grey programming and penalty functions. International Journal of Environmental and Waste Management 2012; 10(2/3): 269-283. https://doi.org/10.1504/IJEWM.2012.048375

Yeomans JS. Water Resources Management Decision- Making Under Stochastic Uncertainty Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives. In Intelligence Systems in Environmental Management: Theory and Applications, IU Sari, C Kahraman (ed.), Springer, Heidelberg, Germany 2017; pp. 207-229. https://doi.org/10.1007/978-3-319-42993-9_10

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