The Optimum Is Not the Goal: Capturing the Decision Space for the Planning of New Neighborhoods

Abstract

This article presents the development and application of a methodology that employs optimization not to seek the single or few optimal plan(s) but to provide planners with a systematic overview of their decision space. As urban development projects are not only subject to decisions of planners but also to those of many actors, the insight about how different actors would decide based on the decisions of the planners should enable planners to already adapt their decisions to ensure that final project targets are reached. The existence of different decision makers is considered via a multiparametric mixedinteger linear programming (mpMILP) approach. Along with the methodology a model was developed, which incorporates multiple domains and scales. Those domains include social, environmental, form, energy, and economic aspects. The considered scales range from single floors up to a neighborhood. Model and methodology were applied to a greenfield development project. Two practical questions are answered, which address the impact of planning decisions about (a) the building density and the sustainability of the energy supply on costs of different actors and (b) the building density and the share of parks on the view on a landmark. The capturing of the decision space revealed trade-offs in terms of chosen energy supply system and urban form, respectively. The presented computational method forms part of the decision support tool URB io , which shall assist urban planning.

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

“…In the search phase of SAGESSE, the user does not repeat this entire process, but nevertheless begins by selecting the project boundaries, and key criteria they wish to explore. Cajot et al (2016) describe in more detail the planning documents analyzed in this phase for a related urban development project, and Schüler et al (2018b) details the components of the resulting mixed integer linear programming (MILP) model. The application of this MILP model with the SAGESSE methodology constitutes the planning support system URB io introduced in Cajot et al (2017c).…”

“…The objectives of this paper are thus (i) to introduce a new interactive optimization methodology addressing the requirements in Figure 2, and (ii) to demonstrate its applicability to a large problem. The case-study used for the second objective relies on the multiparametric mixed integer linear programming approach described by Schüler et al (2018b), applied to the context of urban planning.…”

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

“…Shi et-al [178] highlighted the importance of considering energy interaction among buildings when optimising the energy systems. Schüler et-al [179] tried to optimise the energy system and urban form considering the thermal interactions among buildings and subsequently conduct a comprehensive assessment. However, the impact of urban climate is not considered in this study.…”

Active surfaces selection method for building-integrated photovoltaics (BIPV) in renovation projects based on self-consumption and self-sufficiency

“…The application of this kind of approach is however not relevant in the renovation of buildings. In addition, some studies suggest that in real BIPV installations the optimum is not necessarily the goal [16,35].…”