Abstract
Hubs act as intermediate points for the transfer of materials in the transportation system. In this study, a novel p-mobile hub location–allocation problem is developed. Hub facilities can be transferred to other hubs for the next period. Implementation of mobile hubs can reduce the costs of opening and closing the hubs, particularly in an environment with rapidly changing demands. On the other hand, the movement of facilities reduces lifespan and adds relevant costs. The depreciation cost and lifespan of hub facilities must be considered and the number of movements of the hub’s facilities must be assumed to be limited. Three objective functions are considered to minimize costs, noise pollutions, and the harassment caused by the establishment of a hub for people, a new objective that locates hubs in less populated areas. A multi-objective mixed-integer non-linear programming (MINLP) model is developed. To solve the proposed model, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA-II (KNSGA-II), and a hybrid of K-medoids and MOPSO (KMOPSO) are implemented. The results indicate that KNSGA-II is superior to other algorithms. Also, a case study in Iran is implemented and the related results are analyzed.
Highlights
A hub location problem (HLP) is one of the well-known problems in the location theory
Due to different volumes of a hub activity in each period, because there are different allocated non-hub nodes, the volume of production method is used for calculating the depreciation cost
For selecting an efficient algorithm based on these criteria, a two-sample t -test is implemented and the results are shown in Table 5, which statistically proves that KNSGA-II is better than K-medoids and multi-objective particle swarm optimization (MOPSO) (KMOPSO) regarding quantity and spacing metrics
Summary
A hub location problem (HLP) is one of the well-known problems in the location theory. Rahimi et al (2019) proposed a multi-objective p-HLP, which minimizes the costs while it maximizes the flow between a pair of nodes and minimizes transportation times They proposed a robust-possibilistic programming model to deal with the uncertainties in the system. Zhalechian et al (2017b) proposed a multi-objective p-HLP considering social aspects, responsiveness, and economic under uncertainties and developed a self-adaptive differential evolution (DE) for solving their problem. Another way to deal with uncertainties in location problems is to implement a dynamic facility location (Ghiani et al, 2007). A branch-and-price algorithm is proposed by Catanzaro et al (2011). Danach et al (2019) proposed a Lagrangian relaxation and a hyper-heuristic approach for solving the capacitated HLP
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering Applications of Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
