Review: Optimization and Machine Learning applied to last-Mile logistics

The growth in e-commerce that our society has faced in recent years is changing the view companies have on last-mile logistics due to its increasing impact on the whole supply chain. In addition, new technologies are raising users’ expectations with the need to develop customized delivery experiences; moreover, increasing pressure on supply chains has also created additional challenges for suppliers.

At the same time, this phenomenon generates an increase in the impact on the liveability of our cities due to traffic congestion, the occupation of public spaces, and the environmental and acoustic pollution linked to urban logistics. In this context, optimizing last-mile deliveries is imperative not only for companies with parcels that need to be delivered in the urban areas but also for public administrations that want to guarantee a good quality of life for citizens.

In recent years, many scholars have focused on studying logistics optimization techniques and, in particular, the last mile. However, in addition to traditional optimization techniques linked to the disciplines of operations research, the recent advances in the use of sensors and IoT, and the consequent large amount of data that derives from it, are pushing towards greater use of big data and analytics techniques—such as machine learning and artificial intelligence—which are also in this sector.

Based on this premise, the aim of a research paper by Giuffrida et al. is to provide an overview of the most recent literature advances related to last-mile delivery optimization techniques; this is to be used as a baseline for scholars who intend to explore new approaches and methods in the study of last-mile logistics optimization. Therefore, a bibliometric analysis and a critical review were conducted to highlight the main studied problems, the algorithms used, and the case studies.

The review results of the principal published and indexed articles show an increase in research in the sector in the last few years. In particular, the main techniques used in the case of the machine learning approaches include supervised learning, with a variety of case studies analyzed. In addition, specific attention is paid to the problems of demand forecasting and anomaly detection.

The analysis paves the way for developing and testing innovative unsupervised learning techniques for last-mile logistics. The classic optimization techniques linked to operational research focus on the VRP and its variants, with particular attention to the issues of demand forecasting, reverse logistics, and the multimodal fleet. Moreover, the review shows that new models have been developed that adapt the classic models to real problems and that most of the case studies focus on urban last-mile logistics. Due to high customer demand and the need to improve environmental quality in cities, there is a tendency to create collaborative models between logistics operators, which is one of the main challenges in last-mile logistics today.

Source: Giuffrida N, Fajardo-Calderin J, Masegosa AD, Werner F, Steudter M, Pilla F. Optimization and Machine Learning Applied to Last-Mile Logistics: A ReviewSustainability. 2022; 14(9):5329.

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