A Hierarchical Multi-Objective Programming Approach to Planning Locations for Macro and Micro Fire Stations

TL;DR

This paper introduces a hierarchical multi-objective optimization method for planning the locations of macro and micro fire stations, balancing effectiveness, costs, and operational constraints to improve emergency response.

Contribution

It presents a novel hierarchical optimization framework that considers multiple requirements and functional connections between different fire station levels simultaneously.

Findings

Effective fire station siting in a case study of Shenzhen.

Hierarchical model improves response effectiveness.

Multi-objective algorithms handle complex NP-hard problems.

Abstract

Fire stations are among the most crucial emergency facilities in urban emergency control system in terms of their quick response to fires and other emergencies. Location plannings for fire stations have a significant influence on their effectiveness and capability of emergency responses trading off with the cost of constructions. To obtain efficient and practical siting plans for fire stations, various major requirements including effectiveness maximization, distance constraint and workload limitation are required to be considered in location models, especially for multi-level fire facility systems with macro and micro fire stations having specific aims and construction requirements. This paper proposes a novel hierarchical optimization approach taking all the major requirements for location planning into consideration and bonds functional connections between different levels of fire stations at the same time. A single-objective and a multi-objective optimization model are established to solve the location siting problems for macro and micro fire stations simultaneously. Genetic algorithm with elitist reservation and Pareto-based multi-objective evolutionary algorithm are adopted to solve the problem with NP-hard nature. The proposed hierarchical location model is further performed in a case study of Futian District in Shenzhen, and the siting result justifies the effectiveness and practicality of our novel approach.