Locating a facility to simultaneously address access and coverage goals

Abstract

This paper introduces a bi‐objective strategic location problem to address access and coverage. While previous research in continuous location modelling considered access coverage independently, none has approached such a problem in an integrated fashion. In this paper, a new mathematical formulation considering access and coverage is derived, and a solution algorithm to find the entire set of optimal tradeoff solutions is developed. Application findings are reported for several case studies, varying in problem size. The developed approach is shown to provide critical insights for real‐time decision‐making, facilitating planning, management and policy development.

Urban informatics and spatial optimization

“…With known access, (8), and coverage, (9), for each face, enumeration of all faces κ ∈ Κ will contain the entire set of non-dominated solutions. The proof of this is given in Baik and Murray (2022) along the following lines. Suppose that an optimal soluion (Xt, Y) ≠ (X κ , Y κ ) for one or more κ ∈ Κ.…”

“…For urban informatics, Hong and Murray (2013) and Baik and Murray (2022) are important, illustrating that GIScience may be used explicitly within an exact solution approach, eliminating the need for an exact solver or developed heuristic. Baik and Murray (2022) demonstrate that buffer, overlay and nearest point are fundamentally important. A buffer is a common operation in GIS, representing a derived object based on proximity to/ from an object primitive (e.g., point, line, polygon).…”

“…To illustrate spatial optimization in concrete terms, consider the urban planning problem of siting an industrial facility formalized in Baik and Murray (2022). As (5) µ ← x, y ⊂ R|d (x,y) ≤ S (6)…”

“…The goal in coverage contexts is typically to serve all demand within the stipulated standard or serve as much demand possible given limited resources (see Church & ReVelle, 1974;Church & Murray, 2009). Baik and Murray (2022) note that access and coverage are often considered separately in location decision making. There are many reasons for this, but the point here is simply to indicate that spatial optimization problems are challenging due to many factors that have computational implications: the best strategic location may be anywhere in a region, a so-called continuous space problem; multiple objectives and/or criteria give rise to Pareto or tradeoff solutions; constraining conditions necessitate combinatoric and enumerative exploration; etc.…”

Big in the neighbourhood: Identifying local and regional centres through their network position

“…Public intervention is likely to maintain spatial access to local facilities despite the presumably high cost of maintaining service locations in sparsely populated areas (OECD & JRC, 2021). In fact a sizeable strand of literature focuses on finding societally optimal locations for social services such as postal, health and education facilities (see e.g., Baik & Murray, 2022; Fortney, 1996; Mercier et al, 2021; Pacheco & Casado, 2005). It may therefore be expected that, compared with economic services, social service facilities are somewhat less responsive to local market sizes to ensure adequate spatial accessibility.…”