Bi-objective optimization applying stochastic coverage model

Keywords: mulbi-objective optimization, access, coverage, spatial optimization
Abstract Type: Paper Abstract

Abstract

Location-allocation problems have explored significant topics in geographical issues, including logistics, siting decisions, and other qualities. However, despite the importance of location-allocation problems, considering multiple objects remains challenging, particularly in continuous space. Multi-objective optimization considers multiple objectives simultaneously so that, real-world problems can be reasonably addressed. As Tobler discussed, distance decay plays a significant role in spatial optimization. However, spatial context and other factors can also affect location-allocation problems. For instance, coverage, one of the important spatial considerations, has been mostly dealt with as a binary status (i.e., covered or not). However, the stochastic coverage model can help spatial analysis models.
This proposed research will present the bi-objective optimization problem, which considers both access and coverage in continuous space. Unlike previous studies, we consider coverage to be a stochastic variable that is not only defined by distance but takes into account other factors. This model has the potential to combine spatial context with spatial optimization models. We will present the Pareto frontier of baseball outfielder location sets to solve the multi-objective spatial optimization problem in continuous space. Both coverage and access will be considered simultaneously and the spatial context effect will be also explored for the problem.