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.

中文翻译：

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最后一英里配送模式选择的图像模糊WASPAS方法：以贝尔格莱德为例


由于城市用户数量的增加和交通问题，最后一英里交付（LMD）的要求变得越来越高。此外，医疗危机（如 COVID-19 爆发）和空气污染也是从传统 LMD 模式向社会和环境可持续的 LMD 模式过渡的额外动机。邮政物流行业面临的一个新问题是如何在不确定的多标准环境下确定最佳的 LMD 模式。首次提出了图像模糊环境下加权聚合和积评估（WASPAS）方法的扩展来解决LMD模式选择问题。引入的基于图片模糊集（PFS）的多标准决策（MCDM）方法对于负责LMD的管理者来说非常有益，因为它可以考虑中性/拒绝信息并有效地处理高度不精确的情况、模糊且不确定的信息。与现有最先进的基于 PFS 的 MCDM 方法的比较分析证明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，它还可以应用于解决不确定环境中其他新兴的MCDM问题。结合贝尔格莱德的实际案例，充分说明了图像模糊WASPAS方法的潜力和适用性。结果显示，邮递员是贝尔格莱德 LMD 的最佳模式，其次是货运自行车、无人机、传统送货、自动驾驶车辆和管道运输。