Abstract
Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Addressing these issues, this work introduces SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), a decision support methodology, which relies on interactive multiobjective optimization. Its innovative aspects reside in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the underlying alternative generation process, (ii) a Sobol sequence to efficiently sample the points to explore in the objective space, and (iii) on-the-fly application of multiattribute decision analysis, cluster analysis and other data visualization techniques linked to the parallel coordinates. An illustrative example demonstrates the applicability of the methodology to a large, complex urban planning problem.
Highlights
Making a decision involves balancing multiple competing criteria in order to identify a most-preferred alternative
The goal of this paper is to highlight the current gaps in literature which are limiting the application of interactive optimization (IO) to large problems, and propose a novel methodology addressing these gaps
Interactive optimization consists of four main components which are combined to form a human-computer interaction system: a user, a graphical user interface (GUI), a solution generator and an analyst (Figure 1)
Summary
Making a decision involves balancing multiple competing criteria in order to identify a most-preferred alternative. Multiattribute decision analysis (MADA) aims to help select the best alternative from a predetermined subset (Chankong and Haimes, 2008; Malczewski and Rinner, 2015). Such alternative-focused methods have the risk of Interactive Optimization With Parallel Coordinates omitting important alternatives and leading to suboptimal solutions (Keeney, 1992; Beach, 1993; Belton et al, 1997; Feng and Lin, 1999; Belton and Stewart, 2002; Siebert and Keeney, 2015). The process repeats until the user is convinced to have found the most satisfactory solution
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