Visualizations for decision support in scenario-based multiobjective optimization

Shavazipour, Babooshka; López-Ibáñez, Manuel; Miettinen, Kaisa

doi:10.1016/j.ins.2021.07.025

Cited by 21 publications

(12 citation statements)

References 62 publications

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“…A scenario-based empirical attainment function (SB-EAF) has been proposed by Shavazipour et al (2021b) to support decision-making in multi-scenario multiobjective optimization problems. This function is constructed in the objective space for analyzing a finite set of Pareto optimal solutions.…”

Section: Visual Support For Trade-off Analysismentioning

confidence: 99%

“…For each solution, the SB-EAF value is constructed from s objective vectors, each corresponding to a different scenario (see Shavazipour et al (2021b) for details). SB-EAFs are utilized to visually distinguish different regions of the objective space that may be attained The forest landscape management problem considered here aims at selecting a management regime for each forest stand to optimize predicted long-term outcomes in terms of ecosystem services provided by the landscape.…”

Section: Visual Support For Trade-off Analysismentioning

confidence: 99%

“…To get more insight into the problem and provide better support for the DM in our complex problem, we use the visualization methods proposed by Shavazipour et al (2021b) for decision support in scenario-based multiobjective optimization. Therefore, as proposed by Shavazipour et al (2021b), we start with the all-in-one SB-EAF visualization. We refer to the Pareto optimal solutions obtained in the first and the second iteration as solutions 1 and 2, respectively.…”

Section: Trade-off Analysis Through Visualizationsmentioning

confidence: 99%

“…The multiobjective nature of environmental planning problems combined with their dependence on deep uncertainty brings more complexity (and introduces an additional dimension) to the solution process, making the DM's task cognitively more demanding (Shavazipour et al, 2021b). Recently, different approaches integrated multiobjective optimization and scenario planning to cope with deep uncertainty and help the DM analyze trade-offs between objectives under various scenarios and find the most preferred robust solution (e.g., Watson and Kasprzyk (2017); Eker and Kwakkel (2018); Shavazipour and Stewart (2021); Shavazipour et al (2021a)), forming a class of decision problems called 4 1 INTRODUCTION multi-scenario multiobjective optimization.…”

mentioning

confidence: 99%

“…In this paper, we propose a novel interactive multi-scenario multiobjective optimization approach as a decision support tool for environmental planning problems under deep uncertainty and in particular, sustainable forest management. Our decision support tool includes advanced visualization techniques recently developed for multi-scenario multiobjective optimization problems (Shavazipour et al, 2021b) to analyze trade-offs between objectives in various scenarios. To the best of our knowledge, this is the first study of interactive environmental decision support combining multiobjective trade-offs exploration with multi-scenario considerations, such as optimality/feasibility in any given scenario and robustness over the set of future scenarios.…”

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confidence: 99%

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Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

2022

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Sustainable environmental management often involves long-term time horizons, multiple conflicting objectives, and by nature, is affected by different sources of uncertainty. Many sources of uncertainty, such as climate change or government policies, cannot be addressed using probabilistic models, and, therefore, they can be seen to contain deep uncertainty. In this setting, the variety of possible future states is represented as a set of scenarios lacking any information about the likelihood of occurring. Integrating deep uncertainty into multiobjective decision support increases complexity, calling for the elaboration of appropriate methods and tools. This paper proposes a novel interactive multi-scenario multiobjective approach to support decision-making and trade-off analysis in sustainable forest landscape planning under multiple sources of uncertainty. It includes new preference simulation models aimed at reducing the decision-maker's cognitive load and supporting the preference elicitation process. The proposed approach is applied in a case study of long-term forest landscape planning with four sustainability objectives in twelve scenarios and a forestry expert as the decision-maker. The approach is demonstrated to be efficient in exploring trade-offs in different scenarios, helping the expert gain deep insights into the problem, understand the consequences of alternative strategies, and find the most preferred robust strategy.

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Section: Visual Support For Trade-off Analysismentioning

confidence: 99%

Section: Visual Support For Trade-off Analysismentioning

confidence: 99%

Section: Trade-off Analysis Through Visualizationsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

2022

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Utilizing a projection neural network to convex quadratic multi‐objective programming problems

Jahangiri

Nazemi

2023

Adaptive Control & Signal

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Summary In this paper, we propose a projection neural network model for solving convex quadratic multi‐objective optimization problem (CQMOP). The CQMOP is first converted into an equivalent convex nonlinear programming problem by the means of the weighted sum method, where the Pareto optimal solutions are calculated via different values of weights. A neural network model is then constructed for solving the obtained convex problem. It is shown that the presented neural network is stable in the sense of Lyapunov and is globally convergent. Simulation results are given to illustrate the global convergence and performance of the suggested model. Both theoretical and numerical approaches are studied. Numerical results are in good agreement with the proved theoretical concepts.

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Visualization of Business Processes Through Data Comics

Dospan,

Khrykova

2023

Lecture Notes in Networks and Systems

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Visualizations for decision support in scenario-based multiobjective optimization

Cited by 21 publications

References 62 publications

Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

Utilizing a projection neural network to convex quadratic multi‐objective programming problems

Visualization of Business Processes Through Data Comics

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