This study presents a framework for Visually Interactive Decision-making and Design using Evolutionary Multi-objective Optimization (VIDEO). The VIDEO framework allows users to visually navigate large multi-objective solution sets while aiding decision makers in identifying one or more optimal designs. Specifically, the interactive visualization framework is intended to provide an innovative exploration tool for high-order Pareto-optimal solution sets (i.e., solution sets for three or more objectives). The framework is demonstrated for a long-term groundwater monitoring (LTM) application in which users can explore and visualize tradeoffs for up to four design objectives, simultaneously. Interactive functionality within the framework allows the user to select solutions within the objective space and visualize the corresponding monitoring plan's performance in the design space. This functionality provides the user with a holistic picture of the information provided by a particular solution, ultimately allowing them to make a more informed decision. In addition, the ease with which the framework allows users to navigate and compare solutions as well as design tradeoffs leads to a time efficient analysis, even when there are thousands of potential solutions.

A framework for Visually Interactive Decision-making and Design using Evolutionary Multi-objective Optimization (VIDEO)

SUMMARY: Architecture, Engineering, and Construction (AEC) professionals typically generate and analyze very few design alternatives during the conceptual stage of a project. One primary cause is limitations in the processes and software tools used by the AEC industry. The aerospace industry has overcome similar limitations by using Process Integration and Design Optimization (PIDO) software to support Multidisciplinary Design Optimization (MDO), resulting in a significant reduction to design cycle time as well as improved product performance. This paper describes a test application of PIDO to an AEC case study: the MDO of a classroom building for structural and energy performance. We demonstrate how PIDO can enable orders of magnitude improvement in the number of design cycles typically achieved in practice, and assess PIDO’s potential to improve AEC MDO processes and products.

/pdf/multidisciplinary-process-integration-and-design-45wwjvhb29.pdf

Multidisciplinary process integration and design optimization of a classroom building

In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed framework, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

This paper proposes and demonstrates a new interactive framework for sensitivity-informed de Novo planning to confront the deep uncertainty within water management problems. The framework couples global sensitivity analysis using Sobol' variance decomposition with multiobjective evolutionary algorithms (MOEAs) to generate planning alternatives and test their robustness to new modeling assumptions and scenarios. We explore these issues within the context of a risk-based water supply management problem, where a city seeks the most efficient use of a water market. The case study examines a single city's water supply in the Lower Rio Grande Valley (LRGV) in Texas, using a suite of 6-objective problem formulations that have increasing decision complexity for both a 10-year planning horizon and an extreme single-year drought scenario. The de Novo planning framework demonstrated illustrates how to adaptively improve the value and robustness of our problem formulations by evolving our definition of optimality while discovering key tradeoffs.

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

[1] This study contributes a many-objective analysis of the tradeoffs associated with using the portfolio planning approach for managing the urban water supply risks posed by growing population demands and droughts. The analysis focuses on four supply portfolio strategies: (1) portfolios with permanent rights to reservoir inflows, (2) adaptive options contracts added to the permanent rights, (3) rights, options, and leases, and (4) rights, options, and leases subject to a critical reliability constraint used to represent a maximally risk averse case. The portfolio planning strategies were evaluated using a 10 year Monte Carlo simulation of a city in the Lower Rio Grande Valley (LRGV) within Texas. Our solution sets provide the tradeoff surfaces between portfolios' expected values for cost, cost variability, reliability, surplus water, frequency of using leases, and dropped (or unused) transfers of water. Using an additional severe drought scenario, this work shows that leases and options can reduce the potential for critical supply failures when urban supply systems must contend with unexpected and severe extremes in both demand and water scarcity. In summary, this paper contributes a framework that couples interactive visualization and many-objective optimization to innovate urban water portfolio planning under uncertainty. The many-objective analysis of the LRGV case study shows that effective water portfolio planning can simultaneously improve the costs, efficiency, and reliability of urban water supply while ensuring adaptability and resiliency to future changes.

/pdf/managing-population-and-drought-risks-using-many-objective-403xtfkfej.pdf

Managing population and drought risks using many-objective water portfolio planning under uncertainty

The ARL trade space visualizer: An engineering decision-making tool

We have developed a data visualization interface that facilitates a design by shopping paradigm, allowing a decision-maker to form a preference by viewing a rich set of good designs and use this preference to choose an optimal design Design automation has allowed us to implement this paradigm, since a large number of designs can be synthesized in a short period of time The interface allows users to visualize complex design spaces by using multi-dimensional visualization techniques that include customizable glyph plots, parallel coordinates, linked views, brushing, and histograms As is common with data mining tools, the user can specify upper and lower bounds on the design space variables, assign variables to glyph axes and parallel coordinate plots, and dynamically brush variables Additionally, preference shading for visualizing a user’s preference structure and algorithms for visualizing the Pareto frontier have been incorporated into the interface to help shape a decision-maker’s preference Use of the interface is demonstrated using a satellite design example by highlighting different preference structures and resulting Pareto frontiers The capabilities of the design by shopping interface were driven by real industrial customer needs, and the interface was demonstrated at a spacecraft design conducted by a team at Lockheed Martin, consisting of Mars spacecraft design expertsCopyright © 2003 by ASME

/pdf/design-space-visualization-and-its-application-to-a-design-3d7ncxka6c.pdf

Design Space Visualization and Its Application to a Design by Shopping Paradigm

One of the goals of early stage conceptual design is to execute broad trade studies of possible design concepts, evaluating them for their capability to meet minimum requirements, and choosing the one that best satisfies the goals of the project. To support trade space exploration, we have developed the advanced trade space visualizer (ATSV) that facilitates a design by shopping paradigm, which allows a decision-maker to form a preference a posteriori and use this preference to select a preferred satellite. Design automation has allowed us to implement this paradigm, since a large number of designs can be synthesized in a short period of time. The ATSV uses multidimensional visualization techniques, preference shading, and Pareto frontier display to visualize satellite trade spaces.

Trade space exploration of satellite datasets using a design by shopping paradigm

In this paper, we begin to explore the ramifications of a goal programming approach for multidisciplinary design optimization based on the Collaborative Optimization framework. Working within an existing computing architecture for simulation-based design, we propose a bi-level goal programming formulation of Collaborative Optimization to automate the design synthesis of complex systems, with particular emphasis on undersea vehicles. A brief overview of the simulation-based design computing architecture that has been developed is given. To demonstrate the proposed bi-level goal programming approach, the multidisciplinary design and optimization of an undersea vehicle containing four subsystem level analyses and a system-level analysis is presented. A traditional single-level optimization solution is also presented to provide a benchmark for comparison. While the bi-level goal programming approach is computationally more expensive, it does improve overall system performance by automating subsystem synthesis and arbitration during optimization.

Goal programming applications in multidisciplinary design optimization

This paper describes the conceptual design problem for undersea vehicles. The design problem is cast in a multidisciplinary design optimization framework. We define a contributing synthesis, analogous to a contributing analysis, which performs design synthesis rather than design analysis. The contributing syntheses can be combined into a hierarchical structure that relates high level design requirements to low level technology design rules. We emphasize the form of the contributing syntheses and how they modify the traditional multidisciplinary design optimization problem. In particular, our problem domain is characterized by arbitrary levels of optimization, the prevalence of discrete variables and enumeration as input and output to contributing syntheses, the flexible specification of input and output variables for a contributing synthesis, and the use of inequalities in the coupling constraints between contributing syntheses.

Mike Yukish

Papers

The ARL trade space visualizer: An engineering decision-making tool

Design Space Visualization and Its Application to a Design by Shopping Paradigm

Trade space exploration of satellite datasets using a design by shopping paradigm

Goal programming applications in multidisciplinary design optimization

Requirements on mdo imposed by the undersea vehicle conceptual design problem