Machine learning-based optimisation in a two-echelon logistics network for the dry port operation in China

ABSTRACT

Efforts of creating economic recovery after the COVID-19 pandemic stipulate international logistics demands in the countries and regions affected by China's Belt and Road initiative. Considering the increasing number of small and middle-sized enterprises, there is a great challenge to make transportation plans for the hub port, dry ports, and related enterprises. We investigate a two-echelon vehicle routing problem with simultaneous pickups and deliveries. On one hand, freights are transported from a central depot to multiple satellites, then distributed from the satellites to customers. On the other hand, freights collected from the customers will be loaded at the satellites, then transported back to the depot. We model the problem as mixed integer programming and propose a machine learning-based hybrid algorithm to solve the problem. Our hybrid algorithm comprises a K-Nearest Neighbour algorithm and an Adaptive Large Neighbourhood Search heuristic. We apply our modelling and solution approach to a real case based on the online digital platform ‘Inland Port Cloud Wharf’ in China, which matches international demands of commodities with domestic supplies. Our computational experiments based on a practical case study show the efficiency of our KNN_ALNS algorithm in optimising networks with multimodal coordination and addressing real-world logistics complexities.

KEYWORDS:

• Two-echelon logistics network
• machine learning-based hybrid algorithm
• dry port
• small enterprises
• China’s belt and road initiative

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Shu Zhang, upon reasonable request.

Notes

1 https://neo.lcc.uma.es/vrp/vrp-instances/

2 http://lgimth.xaport.net/#/index

Additional information

Funding

This work was supported by Fundamental Research Funds for the Central Universities: [Grant Number 2021CDJSKJC12 and 2021CDJSKCG34]; National Natural Science Foundation of China: [Grant Number 71971032, 71971033, 72371040, and 72371039].

Notes on contributors

Qin Li

Qin Li is a doctoral student at Chongqing University School of Economics and Business Administration. Her research interests focus on technology-intensive supply chain management. Her publications have appeared in journals such as Annals of Operations Research, Expert Systems with Applications, and Knowledge-Based Systems.

Yu Wang

Yu Wang received the PhD degree in Management Science and Engineering in 2009 from the University of Science and Technology of China. In 2009, he joined the School of Economics and Business Administration, Chongqing University, where he is now a full professor. His current fields of interest include machine learning algorithms and applications, business analytics and business intelligence. In these fields, he has published more than 20 papers in journals such as Information Sciences, Expert Systems with Applications and Knowledge-Based Systems.

Yu Xiong

Yu Xiong is the Associate Vice-President for External Engagement at the University of Surrey. Previously he was Associate Dean international of the University 2020-2022). His research focuses on sustainable and technological issues in global supply chains, where he has published in leading international journals, including the European Journal of Operational Research, International Journal of Production Research, International Journal of Production Economics, and Omega – The International Journal of Management Science, et al.

Shu Zhang

Shu Zhang is an associate professor and doctoral supervisor of the School of Economics and Business Administration at Chongqing University. She got her Ph.D. from the University of Iowa in the United States, majoring in management science. Her research interests include vehicle routing, crowdsourced delivery, and optimizations in city logistics.

Yu Zhou

Yu Zhou is an associate professor and doctoral supervisor of the School of Economics and Business Administration, Chongqing University, and a doctoral candidate jointly trained by Chongqing University and Queen's University Belfast. His research direction focuses on the impact and coordinated development of technological change on the economy, society, and environment. He publishes papers in academic journals such as Management Science, Production and Operations Management, and the European Journal of Operational Research.