A new approach to optimize a hub covering location problem with a queue estimation component using genetic programming

Hub locations are NP-hard problems used in transportation systems. In this paper, we focus on a single-allocation hub covering location problem considering a queue model in which the number of servers is a decision variable. We propose a model enhanced with a queue estimation component to determine...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2018-02, Vol.22 (3), p.949-961
Main Authors: Hasanzadeh, Hamid, Bashiri, Mahdi, Amiri, Amirhossein
Format: Article
Language:English
Subjects:
Airlines
Algorithms
Artificial Intelligence
Computational Intelligence
Control
Costs
Engineering
Genetic algorithms
Heuristic methods
Linear programming
Lower bounds
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Operating costs
Particle swarm optimization
Queuing theory
Random variables
Robotics
Servers
Traffic control
Transportation applications
Transportation systems
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Summary:Hub locations are NP-hard problems used in transportation systems. In this paper, we focus on a single-allocation hub covering location problem considering a queue model in which the number of servers is a decision variable. We propose a model enhanced with a queue estimation component to determine the number and location of hubs and the number of servers in each hub, and to allocate non-hub to hub nodes according to network costs, including fixed costs for establishing each hub and server, transportation costs, and waiting costs. Moreover, we consider the capacity for a queuing system in any hub node. In addition, we present a metaheuristic algorithm based on particle swarm optimization as a solution method. To evaluate the quality of the results obtained by the proposed algorithm, we establish a tight lower bound for the proposed model. Genetic programming is used for lower bound calculation in the proposed method. The results showed better performance of the proposed lower bound compared to a lower bound obtained by a relaxed model. Finally, the computational results confirm that the proposed solution algorithm performs well in optimizing the model with a minimum gap from the calculated lower bound.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-016-2398-1