Locating Urban Service Centers Using a Combined Maximum Coverage, Multi-Objective Programming, and Queueing Theory Approach

This study investigates and optimizes the location of urban service centers in District 3 of Tehran using multi-objective models. The aim is to compare and evaluate the performance of the weighted epsilon-constraint method with genetic algorithms and hybrid particle swarm optimization algorithms to determine the optimal locations for urban service centers and allocate population points to them. The goal is to identify the best locations for a number of service centers that should be established at selected candidate sites.
This model incorporates queueing systems, allowing for a variable number of servers at each center based on customer demand. To address multiple objectives, the epsilon-constraint method is used to solve the model. Finally, metaheuristic methods such as genetic algorithms and particle swarm optimization are employed to assess solution time and model efficiency.
Using these models can reduce waiting times and improve citizens’ access to urban service centers. Moreover, optimizing location and resource allocation can lead to lower operational costs and enhance the overall efficiency of the service delivery system.
The results of this study indicate that employing multi-objective models with a queueing theory approach can effectively improve the location of urban service centers.