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Incorporation of robustness and adaptiveness into reservoir operations under climate change
Published in Ramesh S. V. Teegavarapu, Elpida Kolokytha, Carlos de Oliveira Galvão, Climate Change-Sensitive Water Resources Management, 2020
Young-oh Kim, Seung Beom Seo, Gi Joo Kim
RDM approaches in water resources may vary, mainly because “robustness” can be interpreted differently by stakeholders. Lempert & Groves (2010) suggested a method to incorporate adaptive strategies into the RDM framework to aid decision-makers in designing and evaluating robust strategies. Herman et al. (2015) suggested a taxonomy of robustness framework, which includes the following: (i) identifying alternatives, (ii) sampling states of the world, (iii) quantification of robustness measures, and (iv) identification of key uncertainties (or robustness controls) using sensitivity analysis. RDM is often used collaboratively with Info-Gap Decision Theory (IGDT) (Ben-Haim, 2001) because they share a similar philosophy, which is a non-probabilistic approach to maximize robustness, with the system satisfying the minimum performance requirements. Matrosov et al. (2013) compared RDM with IGDT by applying both methodologies to London’s water supply expansion in the Thames Basin.
Decision support for scenario analysis in a complex water resource project
Published in Journal of Applied Water Engineering and Research, 2021
R. K. Jaiswal, A. K. Lohani, R. V. Galkate
The decision making in complex water resource projects is not easy and needs a supporting tool where simulation can be made can and evaluated based on economic and system performance. Various techniques under exploratory approach were propagated in the past for decision making under deep uncertainty situation including dynamic adaptive policy pathways (DAPP) (Walker et al. 2013; Kwakkel et al. 2015), adaptive policy-making (Kwakkel et al. 2010), real options analysis (ROA); (Jeuland and Whittington 2014; Woodward et al. 2014), info-gap decision theory (Ben-Haim 2006; Matrosov et al. 2013), decision scaling (DS) (Moody and Brown 2013; Steinschneider et al. 2015; Poff et al. 2016), robust decision making (RDM) (Groves and Lempert 2007; Lempert and Collins 2007; Lempert and Collins 2007; Bryant and Lempert 2010; Lempert and Groves 2010; Matrosov et al. 2013; Kim and Chung 2014; Tingstad et al. 2014; Groves et al. 2015), many objective robust decision making (MORDM) (Kasprzyk et al. 2013; Herman et al. 2014; Hadka et al. 2015; Herman et al. 2015), cost effectiveness analysis (CEA) (Liao 2005), portfolio analysis (PA) (Crowe and Parker 2008), multi-criteria decision analysis (MCDA) (Belton and Stewart 2002; Hyde and Maier 2006; Dorini et al. 2011; Kim and Chung 2014), many-objective visual analytics (MOVA); (Matrosov et al. 2015), visually interactive decision-making and design using evolutionary multi-objective optimization (VIDEO) (Kollat and Reed 2007), etc.
Recalibrating the Belt and Road Initiative amidst deep uncertainties
Published in Journal of Mega Infrastructure & Sustainable Development, 2020
Keren Zhu, Rui Shi, Robert Lempert
DMDU methods use models as exploratory tools that track the consequences of assumptions without privileging and particular set of assumptions (Bankes 1993). These include Robust Decision Making (RDM) (Lempert 2002), Many-Objective Robust Decision Making (MORDM) (Kasprzyk et al. 2013), Dynamic Adaptive Planning (DAP) (Walker, Marchau, and Kwakkel 2019), Dynamic Adaptive Policy Pathways (DAPP) (Haasnoot et al. 2013), Info-Gap Decision Theory (IG) (Ben-Haim 2006), Deeply Uncertain Pathways (DUP) (Trindade, Reed, and Characklis 2019), and Engineering Options Analysis (EOA) (Smet 2017). Those methods have been widely applied in decision analysis for mega-project infrastructure planning. For example, Spyrou (2019) used the RDM to study power system planning in Bangladesh through 2016 to 2041, addressing climate change and other socio-economic uncertainties; Trindade et al. (Trindade, Reed, and Characklis 2019) employed the DUP method to identify robust water infrastructure and management policies in the Research Triangle region of North Carolina; Yzer et al. (2014) applied the DAP in a cost-benefit analysis for the planning of Schiphol Airport, Amsterdam (Sidaway et al. 2020).
Untangling decision tree and real options analyses: a public infrastructure case study dealing with political decisions, structural integrity and price uncertainty
Published in Construction Management and Economics, 2019
M. van den Boomen, M. T. J. Spaan, R. Schoenmaker, A. R. M. Wolfert
Section 2 already emphasizes the importance of separating market price uncertainty from other types of uncertainty as they require different treatment in discounting approaches. The case study in Section 2 also demonstrates the difficulty in estimating expected values of the uncertainty variables. Expected values can be obtained by wide range of approaches such as expert judgement, data-analysis, testing, using reference data of similar assets or projects and mathematical prediction modelling. Hereafter, uncertainty bounds for the expected value of variables need to be defined. Again, various approaches are available to model uncertainty bounds such as using random walks (geometric or arithmetic Brownian Motions), shock models, working with (time-variant) probability distributions or with non-probabilistic uncertainty bounds as does the info-gap decision theory and the sensitivity analysis approach. When uncertainty variables influence each other, more sophisticated techniques like Markov chains, Baysian networks, and artificial learning come into view.