<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.</p>
</abstract>

Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.

Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

Department Information Technology Communication and Intellectual Property. Faculty of Law

Assistant Professor, Department of Pharmacology

Doctor of Economics, Professor of management, business organization and innovation Department

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of...

Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

Published Online: Jul 09, 2022

Page range: 177 - 192

Received: Jul 16, 2021

Accepted: Mar 30, 2022

DOI: https://doi.org/10.2478/fcds-2022-0010

Keywords
Location-Assignment, Hub, Reinforced Epsilon Constraint Method, Multilevel Services, Queue Theory, Multi-Objective Optimization

© 2022 Rahmad Syah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

Published Online: Jul 09, 2022

Page range: 177 - 192

Received: Jul 16, 2021

Accepted: Mar 30, 2022

DOI: https://doi.org/10.2478/fcds-2022-0010

KeywordsLocation-Assignment, Hub, Reinforced Epsilon Constraint Method, Multilevel Services, Queue Theory, Multi-Objective Optimization

© 2022 Rahmad Syah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Location-Assignment, Hub, Reinforced Epsilon Constraint Method, Multilevel Services, Queue Theory, Multi-Objective Optimization