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A heuristic algorithm for balancing workloads in coverage modeling
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AI
DOI：10.1016/j.compenvurbsys.2021.101746.png)
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Abstract
En 中文
Location covering problems are important tools for supporting facility siting decisions in both the natural and built environment. However, facility workloads often vary significantly using classic coverage modeling approaches, because service allocations are not explicitly considered. In addition to capacitated extensions that are traditionally relied on, an explicit way to balance workloads in coverage modeling is to track workload difference between pairs of sited facilities, with a goal of minimizing variation. Such an approach provides an effective way to consider workload equity, but a byproduct is increased complexity and computationally difficulty in solving associated models by exact methods. This paper proposes a heuristic algorithm to address the computational difficulties in balancing workloads in coverage modeling. The proposed algorithm incorporates interchange along with simulated annealing, taking advantage of problem-specific knowledge to derive high-quality solutions in an efficient manner. Empirical studies demonstrate that the proposed algorithm is able to generate nondominated solutions that effectively approximate the Pareto optimal frontier, but do so in a computationally efficient manner.
Keywords:
Maximal covering
Workload equity
Bi-objective optimization
Heuristic algorithm
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