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Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks

Badiee, Aghdas, Kalantari, Hamed, Triki, Chefi (2023) Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks. Annals of Operations Research, 322 . pp. 345-383. ISSN 0254-5330. E-ISSN 1572-9338. (doi:10.1007/s10479-022-05029-z) (KAR id:97420)

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One of the key issues in transportation systems is allocating shipping orders to the most appropriate drivers in the shortest time and with the maximum profit. Many studies were carried out in the transportation service procurement process for allocating orders, but none of them considered driver-to-driver interactions and applied information diffusion concepts as a framework to maximize the profit, due to the lack of a framework to model the interactions. In this paper, we present a weighted drivers’ collaboration network to form the interactions. To predict the behavior of drivers, a new community detection algorithm is developed to extract communities and their leaders in terms of the speed and power of receiving and diffusing shipping orders. In addition, we present a profit maximization model that utilizes the information diffusion power of community leaders. The results show the model is able to allocate shipping orders to the most suitable drivers in the shortest possible time and with the highest profit. To demonstrate the performance of the developed algorithm, we present a numerical example. Finally, a case study is applied to solve the optimization problem. The results show that the optimized behavior of companies in allocating orders to drivers is based on their risk level, reputation, and the average number of their customers.

Item Type: Article
DOI/Identification number: 10.1007/s10479-022-05029-z
Uncontrolled keywords: Transportation service procurement; drivers’ collaboration network; diffusion optimization model; overlapping community detection; heterogeneous networks
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: University of Kent (
Depositing User: Chefi Triki
Date Deposited: 14 Oct 2022 08:13 UTC
Last Modified: 08 Nov 2023 00:00 UTC
Resource URI: (The current URI for this page, for reference purposes)
Triki, Chefi:
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