Spatial Optimization for Balancing Workloads in Coverage Modeling

Keywords: covering problem, workload balance, bi-objective, heuristic algorithm
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 02:00 PM (Eastern Time (US & Canada)) - 2/25/2022 03:20 PM (Eastern Time (US & Canada))
Room: Virtual 26

Abstract

Location covering problems are important tools for supporting facility siting decisions in cities. However, facility workloads often vary significantly using classic coverage modeling approaches due to an inability to explicitly account for service allocation. The traditional methods are relied on capacitated extensions and limited in applications due to practical challenges. This paper formulates an explicit model to balance workloads in coverage modeling, which directly minimize the workload difference between pairs of sited facilities. Such an approach provides an effective way to consider workload equity, but a byproduct is increased computational challenges using exact methods. Therefore, a heuristic algorithm is proposed to address the computational difficulties. The algorithm incorporates interchange along with simulated annealing, taking advantage of problem-specific knowledge to derive high-quality solutions efficiently. Empirical studies demonstrate that the proposed algorithm is able to generate non- dominated solutions that effectively approximate the Pareto optimal frontier, but do so in an efficient manner.