Simić Vladimir, Lazarević Dragan, Dobrodolac Momčilo
Department of Postal and Telecommunication Traffic, Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade, 11010 Serbia.
Eur Transp Res Rev. 2021;13(1):43. doi: 10.1186/s12544-021-00501-6. Epub 2021 Jul 30.
Last-mile delivery (LMD) is becoming more and more demanding due to an increasing number of users and traffic problems in cities. Besides, medical crises (like the COVID-19 outbreak) and air pollution represent additional motives for the transition from traditional to socially and environmentally sustainable LMD mode. An emerging problem for companies in the postal and logistics industry is how to determine the best LMD mode in a multi-criteria setting under uncertainty.
For the first time, an extension of the Weighted Aggregated Sum Product ASsessment (WASPAS) method under the picture fuzzy environment is presented to solve the LMD mode selection problem. The introduced picture fuzzy set (PFS) based multi-criteria decision-making (MCDM) method can be highly beneficial to managers who are in charge of LMD since it can take into account the neutral/refusal information and efficiently deal with high levels of imprecise, vague, and uncertain information. The comparative analysis with the existing state-of-the-art PFS-based MCDM methods approved the high reliability of the proposed picture fuzzy WASPAS method. Its high robustness and consistency are also confirmed. The presented method can be used to improve LMD in urban areas worldwide. Besides, it can be applied to solve other emerging MCDM problems in an uncertain environment.
A real-life case study of Belgrade is presented to fully illustrate the potentials and applicability of the picture fuzzy WASPAS method. The results show that postomates are the best mode for LMD in Belgrade, followed by cargo bicycles, drones, traditional delivery, autonomous vehicles, and tube transport.
The online version contains supplementary material available at 10.1186/s12544-021-00501-6.
由于城市中用户数量不断增加以及交通问题，最后一英里配送（LMD）的要求越来越高。此外，医疗危机（如新冠疫情爆发）和空气污染是促使从传统LMD模式向社会和环境可持续的LMD模式转变的额外因素。邮政和物流行业的公司面临的一个新问题是如何在不确定性的多标准环境中确定最佳的LMD模式。
首次提出了一种在图像模糊环境下的加权聚合和积评估（WASPAS）方法的扩展，以解决LMD模式选择问题。引入的基于图像模糊集（PFS）的多标准决策（MCDM）方法对负责LMD的管理人员非常有益，因为它可以考虑中立/拒绝信息，并有效处理高度不精确、模糊和不确定的信息。与现有的基于PFS的先进MCDM方法的比较分析证实了所提出的图像模糊WASPAS方法的高可靠性。其高稳健性和一致性也得到了证实。所提出的方法可用于改善全球城市地区的LMD。此外，它还可应用于解决不确定环境中的其他新出现的MCDM问题。
给出了贝尔格莱德的一个实际案例研究，以充分说明图像模糊WASPAS方法的潜力和适用性。结果表明，邮政配送机器人是贝尔格莱德LMD的最佳模式，其次是货运自行车、无人机、传统配送、自动驾驶车辆和管道运输。
在线版本包含可在10.1186/s12544-021-00501-6获取的补充材料。
