MGA: a decision support system for complex, incompletely defined problems

Brill, E. Downey; Flach, John M.; Hopkins, Lewis D.; Ranjithan, S. Ranji

doi:10.1109/21.105076

Cited by 123 publications

(75 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When formulating real-world problems, emphasis should be placed on generating alternatives that differ in their decision variable performance. In other words, visualizing the decision variable space may be helpful to get "maximally different" alternatives (Brill Jr et al, 1990). While, strictly speaking, problem formulation is concerned with the selection of objective functions, constraints and decision variables, this also has flow-on effects on other aspects of the optimisation process, such as the size of the search space, the characteristics of the fitness function, the ability to find near-optimal solutions and computational efficiency (Figure 3).…”

Section: Changes In Problem Formulationmentioning

confidence: 99%

“…Moreover, a generalized understanding of fitness landscapes should also be linked to decision making processes. While some classes of problems may be easier to solve, they may fail to sufficiently encompass problem traits critical to the actual decision-making process (Brill Jr et al, 1990;Haimes and Hall, 1977;Liebman, 1976;. Formally, understanding the fitness landscape impacts of the formulation changes required for improving decision relevance can highlight needed algorithm innovations.…”

Section: Research Challenges and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

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

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

523

317

View full text Add to dashboard Cite

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.

show abstract

Section: Changes In Problem Formulationmentioning

confidence: 99%

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

523

317

View full text Add to dashboard Cite

show abstract

“…As introduced in the previous work on human-machine systems (human-based engineering or cognitive systems (Brill et al 1990;Woods et al 1990;Jones & Jacobs 2000)), building a decision support system might include interactive or non-interactive optimization and trialand-error experimentation. Our focuses on interactive optimization and the objective of our work could be related to the work of Singh et al (2008).…”

Section: Related Work To Specific Problems Of Interestmentioning

confidence: 99%

“…Thus, especially for complex and/or incompletely defined problems, it is the cooperative problem solving approach (Jones & Jacobs 2000) that supports decision-making, planning or monitoring activities. The concept of joint human-machine cognitive systems (human-based engineering) has been introduced in Woods et al (1990) and Brill et al (1990).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A framework for accurate geospatial modeling using image ranking and machine learning

Bajcsy

Lin

Yahja

et al. 2010

Journal of Hydroinformatics

View full text Add to dashboard Cite

There is a large class of modeling problems where the complexity of the underlying phenomena is overwhelming and hence the accuracy of mathematical models is limited. Our approach to this class of problems is to design frameworks that bring together physically based and data-driven models, and incorporate the tacit knowledge of experts by providing visual exploration and feedback capabilities. This paper presents such a novel computer-assisted framework for accurate geospatial modeling applied to improve groundwater recharge and discharge (R/D) patterns. The novelty of our work is in designing a methodology for ranking and extracting relationships, as well as in developing a general framework for building accurate geospatial models. The framework combines variables derived using physically based inverse modeling with auxiliary geospatial variables directly sensed, ranks variables and extracts variable relationships using data-driven (''machine learning'') techniques, and supports partially expert-driven trial-and-error experimentation and more rigorous optimization, as well as visual explorations, to derive more accurate models for R/D pattern estimation. When the framework was tested by experts, it led to a high level of consistency between the machine-learning-based knowledge and the experts' knowledge about R/D distribution. The prototype solution of the framework is available for downloading at http://isda.ncsa.uiuc.edu/Sp2Learn/.

show abstract

Many‐objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management

Giuliani

Herman

Castelletti

et al. 2014

Water Resources Research

143

View full text Add to dashboard Cite

This study contributes a decision analytic framework to overcome policy inertia and myopia in complex river basin management contexts. The framework combines reservoir policy identification, manyobjective optimization under uncertainty, and visual analytics to characterize current operations and discover key trade-offs between alternative policies for balancing competing demands and system uncertainties. The approach is demonstrated on the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. We have identified a baseline operating policy for the Conowingo Dam that closely reproduces the dynamics of current releases and flows for the Lower Susquehanna and thus can be used to represent the preferences structure guiding current operations. Starting from this baseline policy, our proposed decision analytic framework then combines evolutionary many-objective optimization with visual analytics to discover new operating policies that better balance the trade-offs within the Lower Susquehanna. Our results confirm that the baseline operating policy, which only considers deterministic historical inflows, significantly overestimates the system's reliability in meeting the reservoir's competing demands. Our proposed framework removes this bias by successfully identifying alternative reservoir policies that are more robust to hydroclimatic uncertainties while also better addressing the trade-offs across the Conowingo Dam's multisector services.

show abstract

MGA: a decision support system for complex, incompletely defined problems

Cited by 123 publications

References 26 publications

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

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

A framework for accurate geospatial modeling using image ranking and machine learning

Many‐objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management

Contact Info

Product

Resources

About