Many-objective de Novo water supply portfolio planning under deep uncertainty

Kasprzyk, J. R.; Reed, Patrick M.; Characklis, Gregory W.; Kirsch, Brian R.

doi:10.1016/j.envsoft.2011.04.003

Cited by 128 publications

(78 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This concern has been termed cognitive myopia in the literature exploring biases in decision making (Hogarth andKunreuther 1989, Brill et al 1990). Recent examples of how cognitive myopia can negatively influence decisions have been published for water management as well as for the design of complex engineered systems (Kasprzyk et al 2012. Many-objective robust decision making provides a broader trade-off context, as well as the potential to discover a more diverse suite of management policies to overcome cognitive myopia as suggested by Brill et al (1990).…”

Section: Objectives and Constraintsmentioning

confidence: 99%

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Singh¹,

Reed²,

Keller³

2015

E&S

Self Cite

View full text Add to dashboard Cite

ABSTRACT. Managing ecosystems with deeply uncertain threshold responses and multiple decision makers poses nontrivial decision analytical challenges. The problem is imbued with deep uncertainties because decision makers do not know or cannot converge on a single probability density function for each key parameter, a perfect model structure, or a single adequate objective. The existing literature on managing multistate ecosystems has generally followed a normative decision-making approach based on expected utility maximization (MEU). This approach has simple and intuitive axiomatic foundations, but faces at least two limitations. First, a prespecified utility function is often unable to capture the preferences of diverse decision makers. Second, decision makers' preferences depart from MEU in the presence of deep uncertainty. Here, we introduce a framework that allows decision makers to pose multiple objectives, explore the trade-offs between potentially conflicting preferences of diverse decision makers, and to identify strategies that are robust to deep uncertainties. The framework, referred to as many-objective robust decision making (MORDM), employs multiobjective evolutionary search to identify trade-offs between strategies, re-evaluates their performance under deep uncertainty, and uses interactive visual analytics to support the selection of robust management strategies. We demonstrate MORDM on a stylized decision problem posed by the management of a lake in which surpassing a pollution threshold causes eutrophication. Our results illustrate how framing the lake problem in terms of MEU can fail to represent key trade-offs between phosphorus levels in the lake and expected economic benefits. Moreover, the MEU strategy deteriorates severely in performance for all objectives under deep uncertainties. Alternatively, the MORDM framework enables the discovery of strategies that balance multiple preferences and perform well under deep uncertainty. This decision analytic framework allows the decision makers to select strategies with a better understanding of their expected trade-offs (traditional uncertainty) as well as their robustness (deep uncertainty).

show abstract

Section: Objectives and Constraintsmentioning

confidence: 99%

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Singh¹,

Reed²,

Keller³

2015

E&S

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore an evaluation of robustness measures from different DMMs discovered each DMM ranked solutions to differing performance levels (Herman et al, 2015). Numerous more individual and comparative DMMs studies have been conducted within the context of WRM adaptive planning with specific attention to a measure of robustness (Ghile et al 2014;Haasnoot et al 2013;Jeuland and Whittington 2014;Kwakkel et al 2014;Lempert and Groves 2010;Li, et al 2009;Moody and Brown 2013;Paton et al 2014a;Tingstad et al 2013;Turner et al 2014a;Whateley et al 2014), including investigations into risk-based metrics for analysing adaptation strategy performance (Borgomeo et al 2014;Brown and Baroang 2011;Hall et al 2012a;Kasprzyk et al 2012;Turner et al 2014b) and various new scenario-based methods for ordering and mapping the deep uncertainties within modern WRM problems (Beh et al 2015a;Kang and Lansey 2013;2014;Nazemi et al 2013;Singh et al 2014;Weng et al 2010). However further testing and comparison of DMMs on real world case studies could be highly beneficial especially in regard to evaluating alternative definitions and calculations of system robustness to uncertainty, the methods of scenario generation and the process of adaptation strategy selection and evaluation.…”

Section: The Current Approach Within the Uk As Stated In The Environmentioning

confidence: 99%

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Roach

Kapelan

Ledbetter

et al. 2016

J. Water Resour. Plann. Manage.

View full text Add to dashboard Cite

This paper evaluates two established decision making methods and analyses their performance and suitability within a Water Resources Management (WRM) problem. The methods under assessment are Info-Gap decision theory (IG) and Robust Optimisation (RO). The methods have been selected primarily to investigate a contrasting local vs global method of assessing water system robustness to deep uncertainty but also to compare a robustness model approach (IG) with a robustness algorithm approach (RO), whereby the former selects and analyses a set of prespecified strategies and the latter uses optimisation algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modelling and assesses the applicability of utilising the Future Flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study 2 resembling the Sussex North Water Resource Zone in England, assessing their applicability at improving a risk-based WRM problem and highlighting the strengths and weaknesses of each method at selecting suitable adaptation strategies under climate change and future demand uncertainties. Pareto sets of robustness to cost are produced for both methods and highlight RO as producing the lower costing strategies for the full range of varying target robustness levels. IG produced the more expensive Pareto strategies due to its more selective and stringent robustness analysis, resulting from the more complex scenario ordering process.

show abstract

“…Use of ε-dominance enables a more even search of the objective space and significantly reduces the size of the archive. The ε-NSGAII has been tested in a wide variety of applications and shown to be very efficient and effective for complex optimization problems (e.g., Kollat and Reed 2006;Tang et al 2006;Tang et al 2007;Kasprzyk et al 2009;Kollat et al 2011;Fu et al 2012;Kasprzyk et al 2012). …”

Section: Many-objective Optimization Methodsmentioning

confidence: 99%

“…Recent studies have demonstrated the use of many-objective visual analytics in water supply risk management (Kasprzyk et al 2009;Kasprzyk et al 2012) and groundwater monitoring network design (Kollat et al 2011), and have yielded new design insights and demonstrated the potentially highly negative consequences that could result from lower dimensional formulations. The increase in the number of objectives brings new challenges to multiobjective optimization: deterioration of search ability, exponential increase in non-dominated Pareto approximate solutions and difficulty in solution visualization.…”

Section: N O T C O P Y E D I T E Dmentioning

confidence: 99%

Optimal Design of Water Distribution Systems Using Many-Objective Visual Analytics

Kapelan

Kasprzyk

et al. 2013

J. Water Resour. Plann. Manage.

Self Cite

147

View full text Add to dashboard Cite

This study investigates the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multi-objective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (ε-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age and (6) fire fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This study demonstrates that the many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision making in the WDS design process.

show abstract

Many-objective de Novo water supply portfolio planning under deep uncertainty

Cited by 128 publications

References 59 publications

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Optimal Design of Water Distribution Systems Using Many-Objective Visual Analytics

Contact Info

Product

Resources

About