AUTHORS: Julian Scott Yeomans

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ABSTRACT: Stochastic optimization problems are often overwhelmed with inconsistent performance requirements and incompatible performance specifications that can be difficult to detect during problem formulation. Therefore, it can prove beneficial to create a set of dissimilar options that provide divergent perspectives to the problem. These alternatives should be near-optimal with respect to the specified objective(s), but be maximally different from each other in the decision region. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulationoptimization approaches are commonly employed to solve computationally difficult problems containing significant stochastic uncertainties. This paper provides a new, stochastic, multicriteria MGA approach that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based algorithm.

KEYWORDS: Multicriteria Objectives, Population-based algorithms, Modelling-to-generate-alternatives

REFERENCES:

[ 1] M. Brugnach, A. Tagg, F. Keil, and W.J. De Lange, Uncertainty matters: computer models at the science-policy interface, Water Resources Management, Vol. 21, 2007, pp. 1075-1090.

[2] J.A.E.B. Janssen, M.S. Krol, R.M.J. Schielen, and A.Y. Hoekstra, The effect of modelling quantified expert knowledge and uncertainty information on model-based decision making, Environmental Science and Policy, Vol. 13, No. 3, 2010, pp. 229-238.

[3] M. Matthies, C. Giupponi, and B. Ostendorf, Environmental decision support systems: Current issues, methods and tools, Environmental Modelling and Software, Vol. 22, No. 2, 2007, pp. 123-127.

[4] H.T. Mowrer, Uncertainty in natural resource decision support systems: Sources, interpretation, and importance, Computers and Electronics in Agriculture, Vol. 27, No. 1-3, 2000, pp. 139-154.

[5] W.E. Walker, P. Harremoes, J. Rotmans, J.P. Van der Sluis, M.B.A.P. Van Asselt, Janssen, and M.P. Krayer von Krauss, Defining uncertainty – a conceptual basis for uncertainty management in model-based decision support, Integrated Assessment, Vol. 4, No. 1, 2003, pp. 5-17.

[6] D.H. Loughlin, S.R. Ranjithan, E.D. Brill, and J.W. Baugh, Genetic algorithm approaches for addressing unmodelled objectives in optimization problems, Engineering Optimization, Vol. 33, No. 5, 2001, pp. 549- 569.

[7] J.S. Yeomans, and Y. Gunalay, Simulationoptimization techniques for modelling to generate alternatives in waste management planning, Journal of Applied Operational Research, Vol. 3, No. 1, 2011, pp. 23-35.

[8] E.D. Brill, S.Y. Chang, and L.D. Hopkins, Modelling to generate alternatives: the HSJ approach and an illustration using a problem in land use planning, Management Science, Vol. 28, No. 3, 1982, pp. 221-235.

[9] J.W. Baugh, S.C. Caldwell, and E.D. Brill, A mathematical programming approach for generating alternatives in discrete structural optimization, Engineering Optimization, Vol. 28, No. 1, 1997, pp. 1-31.

[10] E.M. Zechman, and S.R. Ranjithan, Generating alternatives using evolutionary algorithms for water resources and environmental management problems, Journal of Water Resources Planning and Management, Vol. 133, No. 2, 2007, pp. 156-165.

[11] Y. Gunalay, J.S. Yeomans, and G.H. Huang, Modelling to generate alternative policies in highly uncertain environments: An application to municipal solid waste management planning, Journal of Environmental Informatics, Vol. 19, No. 2, 2012, pp. 58-69.

[12] R. Imanirad, and J.S. Yeomans, Modelling to Generate Alternatives Using Biologically Inspired Algorithms, in X.S. Yang (Ed.), Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, Elsevier, Amsterdam, 2013, pp. 313-333.

[13] R. Imanirad, X.S. Yang, and J.S. Yeomans, A computationally efficient, biologically-inspired modelling-to-generate-alternatives method, Journal on Computing, Vol. 2, No. 2, 2012, pp. 43-47.

[14] J.S. Yeomans, An Efficient Computational Procedure for Simultaneously Generating Alternatives to an Optimal Solution Using the Firefly Algorithm, in X.S. Yang (Ed.), NatureInspired Algorithms and Applied Optimization, Springer, New York, 2018, pp. 261-273.

[15] R. Imanirad, X.S. Yang, and J.S. Yeomans, A co-evolutionary, nature-inspired algorithm for the concurrent generation of alternatives, Journal on Computing, Vol. 2, No. 3, 2012, pp. 101-106.

[16] R. Imanirad, X.S. Yang, and J.S. Yeomans, Modelling-to-generate-alternatives via the firefly algorithm, Journal of Applied Operational Research, Vol. 5, No. 1, 2013, pp. 14-21.

[17] R. Imanirad, X.S. Yang, and J.S. Yeomans, A Concurrent Modelling to Generate Alternatives Approach Using the Firefly Algorithm, International Journal of Decision Support System Technology, Vol. 5, No. 2, 2013, pp. 33-45.

[18] R. Imanirad, X.S. Yang, and J.S. Yeomans, A biologically-inspired metaheuristic procedure for modelling-to-generate-alternatives, International Journal of Engineering Research and Applications, Vol. 3, No. 2, 2013, pp. 1677-1686.

[19] J.S. Yeomans, Simultaneous Computing of Sets of Maximally Different Alternatives to Optimal Solutions, International Journal of Engineering Research and Applications, Vol. 7, No. 9, 2017, pp. 21-28.

[20] J.S. Yeomans, An Optimization Algorithm that Simultaneously Calculates Maximally Different Alternatives, International Journal of Computational Engineering Research, Vol. 7, No. 10, 2017, pp. 45-50.

[21] J.S. Yeomans, Computationally Testing the Efficacy of a Modelling-to-GenerateAlternatives Procedure for Simultaneously Creating Solutions, Journal of Computer Engineering, Vol. 20, No. 1, 2018, pp. 38-45.

[22] J.S. Yeomans, A Computational Algorithm for Creating Alternatives to Optimal Solutions, Transactions on Machine Learning and Artificial Intelligence, Vol. 5, No. 5, 2017, pp. 58-68.

[23] J.S. Yeomans, A Simultaneous Modelling-toGenerate-Alternatives Procedure Employing the Firefly Algorithm, in N. Dey (Ed.), Technological Innovations in Knowledge Management and Decision Support, IGI Global, Hershey, Pennsylvania, 2019, pp. 19- 33.

[24] J.S. Yeomans, An Algorithm for Generating Sets of Maximally Different Alternatives Using Population-Based Metaheuristic Procedures, Transactions on Machine Learning and Artificial Intelligence, Vol. 6, No. 5, 2018, pp. 1-9.

[25] J.S. Yeomans, A Bicriterion Approach for Generating Alternatives Using PopulationBased Algorithms, WSEAS Transactions on Systems, Vol. 18, No. 4, 2019, pp. 29-34.

[26] J.S. Yeomans, A Simulation-Optimization Algorithm for Generating Sets of Alternatives Using Population-Based Metaheuristic Procedures, Journal of Software Engineering and Simulation, forthcoming.

[27] M.C. Fu, Optimization for Simulation: Theory vs. Practice, INFORMS Journal on Computing, Vol. 14, No. 3, 2002, pp. 192-215.

[28] P. Kelly, Simulation Optimization is Evolving, INFORMS Journal on Computing, Vol. 14, No. 3, 2002, pp. 223-225.

[29] R. Zou, Y. Liu, J. Riverson, A. Parker, and S. Carter, A Nonlinearity Interval Mapping Scheme for Efficient Waste Allocation Simulation-Optimization Analysis, Water Resources Research, Vol. 46, No. 8, 2010, pp. 1-14.

[30] R. Imanirad, X.S. Yang, and J.S. Yeomans, Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives, in S. Koziel, L. Leifsson, and X.S. Yang (Eds.), Recent Advances in Simulation-Driven Modeling and Optimization, Springer, Heidelberg, Germany, 2016, pp. 299-323.

[31] J.S. Yeomans, Waste Management Facility Expansion Planning Using SimulationOptimization with Grey Programming and Penalty Functions, International Journal of Environmental and Waste Management, Vol. 10, No. 2/3, 2012, pp. 269-283.

[32] J.S. Yeomans, Applications of SimulationOptimization Methods in Environmental Policy Planning Under Uncertainty, Journal of Environmental Informatics, Vol. 12, No. 2, 2008, pp. 174-186.

[33] J.S. Yeomans, and X.S. Yang, Municipal Waste Management Optimization Using a Firefly Algorithm-Driven SimulationOptimization Approach, International Journal of Process Management and Benchmarking, Vol. 4, No. 4, 2014, pp. 363-375.

[34] J.D. Linton, J.S. Yeomans, and R. Yoogalingam, Policy Planning Using Genetic Algorithms Combined with Simulation: The Case of Municipal Solid Waste, Environment and Planning B: Planning and Design, Vol. 29, No. 5, 2002, pp. 757-778.