Multi-objective optimisation model under multiplex weighted drivers’ collaboration network: Risk, time and profit management perspectives

Hamed Kalantari, Aghdas Badiee*, Zahrasadat Tabaie, Mohammad Moshtari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

This study aims to devise a multi-objective optimisation model through the use of drivers’ information diffusion capacity in transportation service procurement [TSP]. In this model, in addition to the profit optimisation of transportation companies, the allocation of freights to the most suitable drivers in the shortest possible time is considered. First, driver-to-driver interactions are modelled on a weighted drivers’ collaboration network and an overlapping community detection algorithm is used to identify the communities’ leaders and their drivers. Also, an objective function is developed to optimise the speed of order allocation to drivers in communities. To reduce the risk of losing customers, the VIKOR method is developed for ranking and allocating shipping orders to the most qualified drivers. This optimisation model enables us to simultaneously manage the three factors of time, cost, and suitability of drivers in allocating shipping orders to drivers. By solving this model, the results show novice companies tend to allocate orders to drivers in the shortest time, but well-known companies are willing to assign the orders with the lowest cost. Therefore, the results reflect the real behaviour of the TSP system, and companies can adopt policies corresponding to their risk level in allocating orders to drivers.

Original languageEnglish
Article number109005
JournalComputers and Industrial Engineering
Volume178
Early online date21 Jan 2023
DOIs
Publication statusPublished - Apr 2023

Keywords

  • Drivers’ collaboration network
  • Graph theory
  • Multi-objective optimisation model
  • Overlapping community detection
  • Transportation service procurement
  • VIKOR

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering

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