RESEARCH ARTICLE


Congestion and Pollution, Vehicle Routing Problem of a Logistics Provider in Thailand



Chanicha Moryadee1, Wissawa Aunyawong1, Mohd Rizaimy Shaharudin2, *
1 College of Logistics and Supply Chain, Suan Sunandha Rajabhat University, Bangkok, Thailand
2 Faculty of Business and Management, Universiti Teknologi MARA, Shah Alam, Malaysia


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Creative Commons License
© 2019 Moryadee et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Faculty of Business and Management, Universiti Teknologi MARA, Malaysia; Tel: 604-44562000;
E-mail: rizaimy@uitm.edu.my


Abstract

Aim and Objective:

This study aims to minimise the travelling distance, operation cost in terms of fuel consumption, and CO2 emissions. It introduces the Time-Dependency Pollution-Routing Problem (TDPRP) with the implementation of the time-dependency and emission model, including constraints such as the limitation of vehicle capacity and vehicle’s speed during different time periods in Thailand. Furthermore, the time window constraint is applied for representing a more realistic model. The main objective is to minimise the total pollution generated because of transportation.

Methods:

The Genetic Algorithm (GA) and Tabu Search (TS) methods have been used to generate the optimal solution with a variety of experiments. The best solutions from all the experiments have been compared to the original solution in terms of the quality of the solution and the computation time.

Results:

The best solution was generated by using the TS method with 30,000 trials. The minimum of the total CO2 emissions was 183.9846 kilograms produced from all of the vehicles during transportation, nearly half from the current transportation plan, which produced 320.94 kilograms of CO2 emissions.

Conclusion:

The proposed model optimised both the route and schedules (multiple time periods) for a number of vehicles, for which the transportation during a fixed congestion period could be predicted to avoid traffic congestion and reduce the CO2 emission. Future research is suggested to add other specific algorithms as well as constraints in order to make the model more realistic.

Keywords: Pollution vehicle routing, Congestion problem, Genetic algorithm, Tabu search, Time-dependency, Emission model, CO2 Emissions, Fuel consumption, Vehicle.