This study focused on the fast vehicle route optimization issue with carbon emission and time window constraints for on-time consumer demand based on the greedy approach. A greedy algorithm was established to rapidly plan the distribution route to obtain the lowest distribution cost and shortest distribution time to achieve a green distribution. The distribution cost model covering the costs of vehicle transportation time, time window deviation, fossil consumption, and PM2.5 emission was established based on on-time demand and green distribution characteristics. This study analyzed the milk distribution route for Guangxi A Diary Co., Ltd. based on the greedy algorithm. The results show that compared with the genetic algorithm, the algorithm running time is reduced by 2 s, although the greedy algorithm requires an additional cost of 21.73 CNY.

1. [1] S. Meishan, “Design of intelligent planning system for tourist scenic route based on ant colony algorithm,” Int. J. of Industrial and Systems Engineering, Vol.39, No.3, pp. 377-393, 2021. https://doi.org/10.1504/IJISE.2021.119712
2. [2] S. Kunnapapdeelert, J. V. Johnson, and P. Phalitnonkiat, “Green last-mile route planning for efficient e-commerce distribution,” Engineering Management in Production and Services, Vol.14, No.1, pp. 1-12, 2022. https://doi.org/10.2478/emj-2022-0001
3. [3] N. Nisrina, M. I. Kemal, I. A. Akbar, and T. Widiani, “The Effect of Genetic Algorithm Parameters Tuning for Route Optimization in Travelling Salesman Problem Through General Full Factorial Design Analysis,” Evergreen, Joint J. of Novel Carbon Resource Sciences & Green Asia Strategy, Vol.9, No.1, pp. 163-203, 2022. https://doi.org/10.5109/4774233
4. [4] W. Tu, “Path optimization of agricultural product logistics distribution based on mileage saving method–Taking S. company as an example,” China Storage and Transportation, No.6, pp. 89-90, 2022. https://doi.org/10.16301/j.cnki.cn12-1204/f.2022.06.112
5. [5] Z. Wei and Y. Xiong, “Research on Chain Convenience Store Distribution Route Optimization Based on Improved Genetic Algorithm,” Logistics Science and Technology, Vol.45, No.4, pp. 33-36, 2022. https://doi.org/10.13714/j.cnki.1002-3100.2022.04.016
6. [6] H. Yang, J. Gao, and E. Shao, “Vehicle Routing Problem with Time Window Considering Delivery Time of Takeaway Orders with One Order and Multiple Products,” Computer Science, Vol.49, No.6A, pp. 191-198, 2022. https://doi.org/10.1016/j.jfoodeng.2006.05.029
7. [7] Y. Xie, “Distribution route optimization algorithm based on hybrid ant colony for multi-temperature zone cold chain logistics,” J. of Shenyang University of Technology, No.5, pp. 552-557, 2022.
8. [8] Q. Hu and J. Lan, “Low-carbon logistics distribution route optimization based on hybrid genetic algorithm,” Logistics Technology, Vol.45, No.4, pp. 18-23, 2022. https://doi.org/10.13714/j.cnki.1002-3100.2022.04.011
9. [9] L. An, T. Ning, X. Song, and J. Wang, “Optimization of Cold Chain Distribution Path of Fresh Agricultural Products Under Carbon Tax Mechanism,” J. of Dalian Jiaotong University, Vol.43, No.1, pp. 105-110, 2022.
10. [10] S. Zhang, C. K. M. Lee, K. L. Choy, W. Ho, and W. H. Ip, “Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem,” Transportation Research Part D Transport & Environment, Vol.31, No.8, pp. 85-99, 2014. https://doi.org/10.1016/j.trd.2014.05.015