Volume 55, Number 4, July-August 2021
|Page(s)||2203 - 2222|
|Published online||29 July 2021|
A model for the time dependent vehicle routing problem with time windows under traffic conditions with intelligent travel times
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
2 Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
3 Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University, Tehran, Iran
* Corresponding author: firstname.lastname@example.org
Accepted: 27 June 2021
A new mixed-integer nonlinear programming model is presented for the time-dependent vehicle routing problem with time windows and intelligent travel times. The aim is to minimize fixed and variable costs, with the assumption that the travel time between any two nodes depends on traffic conditions and is considered to be a function of vehicle departure time. Depending on working hours, the route between any two nodes has a unique traffic parameter. We consider each working day to be divided into several equal and large intervals, termed as a time interval of traffic. Here, allowing for long distances between some of the nodes, travel time may take more than one time interval of traffic, resulting in resetting the time interval of traffic at the start of each large interval. This repetition of time interval of traffics has been used in modeling and calculating travel time. A tabu search optimization algorithm is devised for solving large problems. Also, after linearization, a number of random instances are generated and solved by the CPLEX solver of GAMS to assess the effectiveness of our proposed algorithm. Results indicate that the initial travel time is estimated appropriately and updated properly in accordance with to the repeating traffic conditions.
Mathematics Subject Classification: 90Cxx
Key words: Vehicle routing problem / time windows / traffic condition / time-dependent
© The authors. Published by EDP Sciences, ROADEF, SMAI 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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