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Title: Developing a multi-commodity multi-period mathematical model based on the travelling salesman problem for solving bike sharing rebalancing problem

Authors: Bahareh Mohammadi; Hadi Shirouyehzad

Addresses: Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran ' Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Abstract: The rebalance problem in the bike sharing system includes operational decisions to respond to demand fluctuations in the kidneys Bicycle stations, so that by redistributing bikes among the stations with a balancing fleet, users can be satisfied. In recent years, considering the cities growth and development and also traffic challenges it has been one of the major needs of the urban transportation network. In particular, the use of bicycles on intra-city trips In addition to reducing the heavy traffic volume, it also leads to positive ecological and environmental impacts. On the other hand, combining this balancing process with minimal cost can have positive economic effects for communities, and encourage users to use these systems more frequently. In this research, the development of a mathematical model based on travelling salesman problem (TSP) has been devised that this is multi-objective model, with considering excessive constraints and the lack of permissible and different types of bikes at these stations, and in multi-period mode, is able to minimise slacks and surpluses in stations, in addition to minimising stations balancing costs. Also, to validate the model and demonstrate its efficiency and performance, comprehensive examples have been solved using an exact solution approach.

Keywords: bike sharing system; bike sharing rebalancing problem; multi objective mathematical model; vehicle routing problem.

DOI: 10.1504/IJMOM.2018.095668

International Journal of Modelling in Operations Management, 2018 Vol.7 No.1, pp.59 - 74

Received: 28 Dec 2017
Accepted: 07 Mar 2018

Published online: 16 Oct 2018 *

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