Optimal decision model and improved genetic algorithm for disposition of hierarchical facilities under hybrid service availability

Abstract

Hierarchical covering location problems (HCLPs) have been extensively constructed to meet the requirements of different levels of services within a budget, but lack basic relationship analysis among the service level, budget, and facility quantity; moreover, the genetic algorithms (GAs) that solve the HCLPs are slow and complex. To focus on these problems and establish an optimized HCLP and improved GA, in this study, we carried out numerical simulations and a full-scale experiment in order to investigate the efficiency of functional relations in reaching the most efficient coverage performance by hybrid service availability, and investigate the efficiency of high solution quality by constructing a new chromosome-encoding method and corresponding operators. The results demonstrate that the hybrid hierarchy provides more facility schemes and more location schemes compared to other service availabilities. This mode effectively maintains higher coverage performance within the same budget, but it does not always present the lowest construction costs when maintaining the most efficient coverage performance. Furthermore, the accuracy and efficiency of the suggested GA could be dramatically improved compared to the accurate algorithm and binary coding GA. An HCLP was eventually proposed for scenarios of adding capacity constraints, including further consideration related to hybrid service availability.