Distance-based and stochastic uncertainty analysis for multi-criteria decision analysis in Excel using Visual Basic for Applications

Hyde, Kylie; Maier, Holger R.

doi:10.1016/j.envsoft.2005.08.004

Cited by 83 publications

(44 citation statements)

References 24 publications

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“…However, the development of more formal approaches for achieving this, such as scenario discovery (e.g. Kasprzyk et al, 2013;Lempert and Groves, 2010) and multi-criteria decision analysis (Hyde and Maier, 2006;Korteling et al, 2013), could be investigated in future research, especially for more complex problems. In addition, even though adaptability is considered post-optimisation, it is not included as part of the formal optimal sequencing process in this study.…”

Section: Discussionmentioning

confidence: 99%

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Scenario driven optimal sequencing under deep uncertainty

Beh

Maier

Dandy

2015

Environmental Modelling & Software

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After the embargo period  via non-commercial hosting platforms such as their institutional repository  via commercial sites with which Elsevier has an agreement In all cases accepted manuscripts should: link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe optimal sequencing / scheduling of activities is vital in many areas of environmental and water resources planning and management. In order to account for deep uncertainty surrounding future conditions, a new optimal scheduling approach is introduced in this paper, which consists of three stages. Firstly, a portfolio of diverse sequences that are optimal under a range of plausible future conditions is generated. Next, global sensitivity analysis is used to assess the robustness of these sequences and to determine the relative contribution of future uncertain variables to this robustness. Finally, an optimal sequence is selected for implementation. The approach is applied to the optimal sequencing of additional potential water supply sources, such as desalinated-, stormand rain-water, for the southern Adelaide water supply system, over a 40 year planning horizon at 10-year intervals. The results indicate that the proposed approach is useful in identifying optimal sequences under deep uncertainty.

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Section: Discussionmentioning

confidence: 99%

“…Depending on the complexity of the problem, this decision-making process can be undertaken informally or using more formal approaches, such as multi-criteria decision analysis (see Hyde and Maier, 2006;Korteling et al, 2013).…”

Section: Selection Of Optimal Sequencementioning

confidence: 99%

Scenario driven optimal sequencing under deep uncertainty

Beh

Maier

Dandy

2015

Environmental Modelling & Software

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“…This is further addressed in Section 3.7 and in the multiple-criteria decision making literature (e.g. Hyde and Maier, 2006;Hyde et al, 2004;Hyde et al, 2005). Additionally, there should be further work on linking the properties of algorithms to the number of objectives, especially with the increasing consideration of larger numbers of objectives when optimising real-world problems.…”

Section: Research Challenges and Future Directionsmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

Self Cite

532

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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“…Relation with data envelopment analysis Investigation of the improvement and deterioration of the alternative's performances and/or comprehensive value that grants, respectively, achievement or maintenance of some target result has received a limited attention in MCDA (see [5,17,39,41], and a review in Sect. 4).…”

Section: Indirect and Imprecise Preference Informationmentioning

confidence: 99%

“…Serving as a tool for evaluating and comparing alternatives, a criterion represents a specific point of view on the impact and quality of alternative decisions. As noted by Hyde and Maier [17], the performance values that are assigned to each alternative for every criterion are obtained from models, available data, or by expert judgment based on previous knowledge or experience.…”

Section: Introductionmentioning

confidence: 99%

Post factum analysis for robust multiple criteria ranking and sorting

et al. 2015

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Providing partial preference information for multiple criteria ranking or sorting problems results in the indetermination of the preference model. Investigating the influence of this indetermination on the suggested recommendation, we may obtain the necessary, possible and extreme results confirmed by, respectively, all, at least one, or the most and least advantageous preference model instances compatible with the input preference information. We propose a framework for answering questions regarding stability of these results. In particular, we are investigating the minimal improvement that warrants feasibility of some currently impossible outcome as well as the maximal deterioration by which some already attainable result still holds. Taking into account the setting of multiple criteria ranking and sorting problems, we consider such questions in view of pairwise preference relations, or attaining some rank, or assignment. The improvement or deterioration of the sort of an alternative is quantified with the change of its performances on particular criteria and/or its comprehensive score. The proposed framework is useful in terms of design, planning, formulating the guidelines, or defining the future performance targets. It is also important for robustness concern because it finds which parts of the recommendation are robust or sensitive with respect to the modification of the alternatives' performance values or scores. Application of the proposed approach is demonstrated on the problem of assessing environmental impact of main European cities.

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Distance-based and stochastic uncertainty analysis for multi-criteria decision analysis in Excel using Visual Basic for Applications

Cited by 83 publications

References 24 publications

Scenario driven optimal sequencing under deep uncertainty

Scenario driven optimal sequencing under deep uncertainty

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Post factum analysis for robust multiple criteria ranking and sorting

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