A Survey of Crowdsourcing in Last-Mile Delivery in the Optimization Literature

The rapid expansion of online sales has been facilitated by digital platforms that allow for the examination, comparison, and procurement of an extensive range of goods. To remain viable, online merchants are compelled to engage in competitive practices. Data reveals that online sales of material goods in the United States amounted to $875.2 billion in 2022, with projections indicating a rise to $1,329.7 billion by the year 2025. This growth trajectory has heightened the competition surrounding delivery timeframes. The ubiquity of smartphones and internet connectivity has amplified business-to-customer (B2C) e-commerce activities, encompassing both the purchase and sale of items online. E-commerce constitutes a critical component of the global retail sector and is forecasted to experience an increase of $10.57 billion from 2020 to 2025. The surge in e-commerce has concomitantly altered delivery expectations and exerted pressure on Last-Mile Delivery (LMD) systems. Despite the extensive scrutiny of LMD, there remains an absence of comprehensive, systematic reviews on the subject within the existing body of literature. Accordingly, this review paper embarks on an examination of LMD research within the realm of optimization literature. It identifies a multitude of research gaps that present opportunities for the further application of LMD, focusing on aspects such as objective functions, sensitivity analyses, and the potential for integration with other challenges. The paper aims to serve as an exhaustive and methodical review that can act as a directional compass for subsequent research endeavors.