چکیده:
Background and Objectives: 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. Method: 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. Findings: 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. Results: 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.
خلاصه ماشینی:
The aim of this research is to compare and evaluate the performance of the weighted epsilon-constraint method with the genetic algorithm and the hybrid particle swarm optimization algorithm to determine the optimal location of urban service centers and the allocation of population points to them.
Location of urban service centers with a combined approach of maximum coverage, multi-objective programming, and queuing theory Received: 2024/10/26 Accepted: 2025/02/08 Research Article Type pp.
In each center, based on the needs of the region, the number of servers is determined hypothetically so that the model's objectives, including reducing the time distance of service applicants to the centers and minimizing the average server idleness, are achieved.
The main objectives of this research include minimizing waiting time in service centers using queuing theory and maximizing the population covered by each created facility considering the coverage radius of each center.
| Location of urban service centers with a combined approach of maximum coverage, multi-objective programming, and queuing theory | Shondi and Mahlouji | Single server | Maximizing the population under coverage | Solving fuzzy model with genetic algorithm | Abolian et al.
| M/M/k | Minimizing waiting and travel times for all customers | Metaheuristic algorithm | Present research | M/M/k | Maximizing the population under coverage | Service quality | Metaheuristic algorithms | Human Resource Management Development and Support Quarterly, 20th Year, No. 75, Spring 2025) In the facility location process, usually more than one decision-maker is involved.
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