Including robustness considerations in the search phase of Many-Objective Robust Decision Making

Eker, Sibel; Kwakkel, Jan H.

doi:10.1016/j.envsoft.2018.03.029

Cited by 39 publications

(27 citation statements)

References 41 publications

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“…This signified the importance of focusing on robustness rather than optimality in decision support when uncertainty exists, also emphasised in the literature (see e.g. (Eker & Kwakkel 2018;Hamarat et al 2014;Moallemi, Elsawah & Ryan 2018)). Given the benefits and drawbacks of each approach, we acknowledge that both approaches can benefit the decision maker in different under different conditions.…”

Section: Discussionmentioning

confidence: 93%

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Comparing Implications of ‘Robustness’ and ‘Optimality’ for Decision Support under Uncertainty

Turan

Elsawah

Ryan

2019

INCOSE International Symp

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Conventional simulation‐optimisation approach in decision support under uncertainty aims to find optimal decisions which can meet decision objective(s) under reference scenarios with well‐characterised uncertainty. However, futures often emerge with unexpected circumstances which cannot be predicted in our pre‐specified reference scenarios, and their uncertainty cannot be fully characterised a priori. This article uses robust optimisation based on simulation models as an alternative approach to address such uncertainties of decision support. To show how the two approaches work and how better robust optimisation can be, we formulate a fleet mix problem under uncertainty and implement it in two experiments to compare the results. We conclude that robust optimisation could lead to decisions with probably lower performance than conventional optimisation at imagined future scenarios. However, the advantage of robust optimisation is that the results have better performance in overall and are more reliable over a wide variety of scenarios in the future.

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Section: Discussionmentioning

confidence: 93%

“…decision and scenario) is valid. Several examples of the use of robust optimisation in robust decision support frameworks can be found in previous studies (Eker & Kwakkel 2018;Herman et al 2015;Moallemi et al 2018;Moallemi, Elsawah & Ryan 2018).…”

Section: Introductionmentioning

confidence: 99%

Comparing Implications of ‘Robustness’ and ‘Optimality’ for Decision Support under Uncertainty

Turan

Elsawah

Ryan

2019

INCOSE International Symp

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“…Many-objective robust decision making (MORDM; Kasprzyk et al, 2013) extends the exploratory modeling concepts by including multi-objective evolutionary optimization to discover candidate actions and characterize how their vulnerabilities and trade-offs vary across deeply uncertain futures. More recently, several studies (Eker & Kwakkel, 2018;Trindade et al, 2017;Watson & Kasprzyk, 2017) have demonstrated that the inclusion of deep uncertainties in the search phase of the MORDM framework can improve solutions' robustness and discovered performance trade-offs between conflicting objectives.…”

Section: Research Articlementioning

confidence: 99%

Identifying Actionable Compromises: Navigating Multi‐City Robustness Conflicts to Discover Cooperative Safe Operating Spaces for Regional Water Supply Portfolios

Gold

Reed

Trindade

et al. 2019

Water Resources Research

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Regional cooperation among water utilities can improve the robustness of urban water supply systems to challenging and deeply uncertain futures. Through coordination mechanisms such as water transfers and regional demand management, water utilities can improve the efficiency of resource allocation and delay the need for new infrastructure investments. Though cooperation provides utilities with the potential for reduced cost strategies for improving the reliability of their services, two important challenges are worthy of careful consideration. First, regional utilities often have to navigate robustness conflicts stemming from potential asymmetries in their risk exposure, demand dynamics, and the availability of supply resources. Second, successful implementation of candidate compromise water portfolios requires that cooperating utilities understand their operational tolerances to deviations from the recommended regional actions (“imperfect implementation”). This study contributes a framework for identifying compromises across regional robustness conflicts and quantifying tolerances to implementation uncertainties. The framework is demonstrated on a system of four interdependent but institutionally independent water utilities in the Research Triangle region of North Carolina that are confronting robustness conflicts given asymmetries in their vulnerabilities to growing demands and hydroclimatic uncertainties. Our findings highlight that seemingly balanced compromise management strategies can yield significant and potentially surprising unintended consequences that could degrade regional cooperation. Moreover, asymmetries in regional robustness can be amplified with modest deviations from their agreed upon actions. Results of this analysis are broadly applicable to water supply regionalization as a global challenge and provide insights for discovering robust compromises and safe operating spaces for multi‐actor water supply systems.

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“…We provided a proof of principle implementation by adapting ε-NSGAII (Kollat & Reed, 2006). The MOEA approach enables easy extensions by bringing in additional scenario relevant considerations such as diversity and consistency (Carlsen, Lempert, Wikman-Svahn, & Schweizer, 2016;Eker & Kwakkel, 2018). Within an MOEA framework, these can easily be added as additional objectives, rather than be calculated in postprocessing as with PRIM.…”

Section: Con Clus Ionmentioning

confidence: 99%

A generalized many‐objective optimization approach for scenario discovery

Kwakkel

2019

Futures & Foresight Science

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Scenario discovery is a model‐based approach for scenario development, aimed at finding one or more subspaces within the uncertainty space associated with a model that is decision‐relevant. These identified subspaces can subsequently be translated into narratives or shared in other ways a broader participatory process. Finding such as subspace involves solving a three‐objective optimization problem. A subspace should cover many of the decision relevant model runs, while containing as few as possible nondecision relevant model runs, and being easy to interpret. Existing techniques for scenario discovery, however, focus only on finding a subspace that minimizes the number of nondecision relevant model runs. Adopting a single objective optimization approach for a many‐objective optimization problem implies that the full trade‐off space is not identified. In this paper, we introduce a many‐objective optimization approach for scenario discovery. We compare this with an improved usage of Patient Rule Induction Method (PRIM) for identifying the multidimensional trade‐offs amongst coverage, density, and interpretability. We find that the many‐objective optimization approach produces results which slightly dominate those of the improved version of PRIM on all three objectives. Qualitatively, however, both approaches identify essentially the same subspaces. The prime benefits of the many‐objective optimization approach are its potential in bringing additional scenario relevant concerns such as consistency or diversity into the scenario discovery framework, as well as its ability to avoid overfitting. Potentially more important, it also paves the way for future work on using more sophisticated metaheuristic optimization approaches for scenario discovery.

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Including robustness considerations in the search phase of Many-Objective Robust Decision Making

Cited by 39 publications

References 41 publications

Comparing Implications of ‘Robustness’ and ‘Optimality’ for Decision Support under Uncertainty

Comparing Implications of ‘Robustness’ and ‘Optimality’ for Decision Support under Uncertainty

Identifying Actionable Compromises: Navigating Multi‐City Robustness Conflicts to Discover Cooperative Safe Operating Spaces for Regional Water Supply Portfolios

A generalized many‐objective optimization approach for scenario discovery

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