A machine learning optimization approach for last-mile delivery and third-party logistics

Bruni, Maria Elena; Fadda, Edoardo; Fedorov, Stanislav; Perboli, Guido

doi:10.1016/j.cor.2023.106262

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…Similarly, each column of the cost matrix is reduced by subtracting the minimum cost in that column from all the elements in the column. This ensures that at least one zero is present in each column, as can be observed in Equation (5).…”

Section: Minimize Each Row: Rowmentioning

confidence: 99%

“…However, optimizing delivery routes presents a formidable challenge, necessitating consideration of dynamic variables such as traffic patterns and delivery windows. Moreover, the seamless facilitation of communication and tracking throughout the delivery journey is essential for ensuring a smooth and transparent customer experience [4,5]. These multifaceted challenges collectively underscore the pressing need for innovative logistical solutions and strategies that harness the potential of smart transportation and mobility technologies to maintain competitiveness in the ever-evolving landscape of smart e-commerce and logistics.…”

Section: Introductionmentioning

confidence: 99%

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Smart Delivery Assignment through Machine Learning and the Hungarian Algorithm

Vásconez,

Schotborgh,

Vásconez

et al. 2024

Smart Cities

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Intelligent transportation and advanced mobility techniques focus on helping operators to efficiently manage navigation tasks in smart cities, enhancing cost efficiency, increasing security, and reducing costs. Although this field has seen significant advances in developing large-scale monitoring of smart cities, several challenges persist concerning the practical assignment of delivery personnel to customer orders. To address this issue, we propose an architecture to optimize the task assignment problem for delivery personnel. We propose the use of different cost functions obtained with deterministic and machine learning techniques. In particular, we compared the performance of linear and polynomial regression methods to construct different cost functions represented by matrices with orders and delivery people information. Then, we applied the Hungarian optimization algorithm to solve the assignment problem, which optimally assigns delivery personnel and orders. The results demonstrate that when used to estimate distance information, linear regression can reduce estimation errors by up to 568.52 km (1.51%) for our dataset compared to other methods. In contrast, polynomial regression proves effective in constructing a superior cost function based on time information, reducing estimation errors by up to 17,143.41 min (11.59%) compared to alternative methods. The proposed approach aims to enhance delivery personnel allocation within the delivery sector, thereby optimizing the efficiency of this process.

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Section: Minimize Each Row: Rowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Delivery Assignment through Machine Learning and the Hungarian Algorithm

Vásconez,

Schotborgh,

Vásconez

et al. 2024

Smart Cities

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show abstract

“…Direction or Application Fields [197,198] Machine learning optimization Last-mile delivery, third-party logistics, and bin packing problem [199] Comparative…”

Section: References Modelsmentioning

confidence: 99%

A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

Zhang,

Guo,

Chen

et al. 2023

JTAER

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The rapid growth of e-commerce has significantly increased the demand for advanced techniques to address specific tasks in the e-commerce field. In this paper, we present a brief survey of machine learning and deep learning techniques in the context of e-commerce, focusing on the years 2018–2023 in a Google Scholar search, with the aim of identifying state-of-the-art approaches, main topics, and potential challenges in the field. We first introduce the applied machine learning and deep learning techniques, spanning from support vector machines, decision trees, and random forests to conventional neural networks, recurrent neural networks, generative adversarial networks, and beyond. Next, we summarize the main topics, including sentiment analysis, recommendation systems, fake review detection, fraud detection, customer churn prediction, customer purchase behavior prediction, prediction of sales, product classification, and image recognition. Finally, we discuss the main challenges and trends, which are related to imbalanced data, over-fitting and generalization, multi-modal learning, interpretability, personalization, chatbots, and virtual assistance. This survey offers a concise overview of the current state and future directions regarding the use of machine learning and deep learning techniques in the context of e-commerce. Further research and development will be necessary to address the evolving challenges and opportunities presented by the dynamic e-commerce landscape.

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“…The study by Bruni et al [7] explores the challenges and solutions in the last-mile delivery sector, which is affected by the growing urban population and increased demand for services and goods. The study focuses on the adoption of third-party logistics (3PL) as a management solution to enhance efficiency and mitigate uncertainties.…”

Section: Related Workmentioning

confidence: 99%

Incorporating Time-Series Forecasting Techniques to Predict Logistics Companies’ Staffing Needs and Order Volume

Alqatawna,

Abu-Salih,

Obeid

et al. 2023

Computation

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Time-series analysis is a widely used method for studying past data to make future predictions. This paper focuses on utilizing time-series analysis techniques to forecast the resource needs of logistics delivery companies, enabling them to meet their objectives and ensure sustained growth. The study aims to build a model that optimizes the prediction of order volume during specific time periods and determines the staffing requirements for the company. The prediction of order volume in logistics companies involves analyzing trend and seasonality components in the data. Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) are well-established and effective in capturing these patterns, providing interpretable results. Deep-learning algorithms require more data for training, which may be limited in certain logistics scenarios. In such cases, traditional models like SARIMAX, ARIMA, and AR can still deliver reliable predictions with fewer data points. Deep-learning models like LSTM can capture complex patterns but lack interpretability, which is crucial in the logistics industry. Balancing performance and practicality, our study combined SARIMAX, ARIMA, AR, and Long Short-Term Memory (LSTM) models to provide a comprehensive analysis and insights into predicting order volume in logistics companies. A real dataset from an international shipping company, consisting of the number of orders during specific time periods, was used to generate a comprehensive time-series dataset. Additionally, new features such as holidays, off days, and sales seasons were incorporated into the dataset to assess their impact on order forecasting and workforce demands. The paper compares the performance of the four different time-series analysis methods in predicting order trends for three countries: United Arab Emirates (UAE), Kingdom of Saudi Arabia (KSA), and Kuwait (KWT), as well as across all countries. By analyzing the data and applying the SARIMAX, ARIMA, LSTM, and AR models to predict future order volume and trends, it was found that the SARIMAX model outperformed the other methods. The SARIMAX model demonstrated superior accuracy in predicting order volumes and trends in the UAE (MAPE: 0.097, RMSE: 0.134), KSA (MAPE: 0.158, RMSE: 0.199), and KWT (MAPE: 0.137, RMSE: 0.215).

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A machine learning optimization approach for last-mile delivery and third-party logistics

Cited by 11 publications

References 31 publications

Smart Delivery Assignment through Machine Learning and the Hungarian Algorithm

Smart Delivery Assignment through Machine Learning and the Hungarian Algorithm

A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

Incorporating Time-Series Forecasting Techniques to Predict Logistics Companies’ Staffing Needs and Order Volume

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