The world faced a major disruption in the form of the coronavirus disease (COVID-19) pandemic, which caused many countries to impose severe restrictions on movement, popularly known as “lockdown.” These lockdowns impacted transportation adversely, leading to massive disruptions in global and local supply chains. As the local markets were shut down, more people started turning to e-commerce logistics platforms offering doorstep deliveries of essential items (food and medicines). This resulted in an explosion in demand for such services, and businesses struggled to complete their deliveries. Additionally, the volume of real-time text data suddenly increased, as these customers started sharing their feedback on social media platforms. The availability of real-time raw text data and its popularity for solving complex business problems motivated the development of the approach proposed herein to address last-mile delivery issues. Thus, this paper suggests the use of Twitter data to identify the various grievances of customers about e-commerce logistics platforms. Natural language processing, a popular tool for text analytics, is employed to extract consumer tweets from the Twitter profiles of such businesses and subsequently to clean, process, and analyse them. Issues are categorized and used as objectives in a multiobjective fuzzy vehicle routing problem (VRP). An integrated hybrid fuzzy VRP is developed and coded to solve last-mile delivery issues. Experimental results and comparative analyses highlight the benefits of the novel approach. Managerial insights and scope for future research assist in the further development of the idea.

中文翻译：

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使用 Twitter 数据的多目标模糊车辆路由：重新构想必需品的交付

世界面临着冠状病毒病 (COVID-19) 大流行形式的重大破坏，这导致许多国家对行动实施严格限制，即俗称的“封锁”。这些封锁对交通运输产生了不利影响，导致全球和本地供应链出现大规模中断。随着当地市场的关闭，越来越多的人开始转向电子商务物流平台，该平台可现场运送必需品（食品和药品）。这导致对此类服务的需求激增，企业难以完成交付。此外，实时文本数据量突然增加，因为这些客户开始在社交媒体平台上分享他们的反馈。实时原始文本数据的可用性及其在解决复杂业务问题方面的普及推动了本文提出的解决最后一英里交付问题的方法的发展。因此，本文建议使用 Twitter 数据来识别客户对电子商务物流平台的各种不满。自然语言处理是一种流行的文本分析工具，用于从此类企业的 Twitter 个人资料中提取消费者推文，然后对其进行清理、处理和分析。问题被分类并用作多目标模糊车辆路径问题 (VRP) 中的目标。开发和编码集成混合模糊 VRP 以解决最后一英里交付问题。实验结果和比较分析突出了新方法的好处。