Integrating Internet of Things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application

Abstract

With the rapid growth of perishable food e-commerce businesses, there is a definite need for logistics services providers to manage parcel shipments with multi-temperature requirements. E-commerce characteristics, including time-critical delivery, fragmented orders, and high product variety, should be further considered to extend the ontology of multi-temperature joint distribution. However, traditional delivery route planning is insufficient because it merely minimises the cost of travelling between customer locations. Factors related to food quality and arrival time windows should also be considered. In addition, handling dynamic incident management, such as violations of handling requirements during delivery, is lacking. This leads to the likelihood of food deteriorating before it reaches the consumers, thereby impacting customer satisfaction. This paper proposes an Internet of Things–based multi-temperature delivery planning system (IoT-MTDPS), embedding a two-phase multi-objective genetic algorithm optimiser (2PMGAO). The formulation of delivery routing mainly considers product-dependent multi-temperature characteristics, service level, transportation cost, and number of trucks. Once there are unexpected incidents which are detected by Internet of Things technologies, 2PMGAO can optimise the membership functions of fuzzy logic for re-routing the e-commerce delivery plan. With using IoT-MTDPS, the capability of handling e-commerce orders is enhanced, while customer satisfaction can be maintained at a designated level.

KEYWORDS:

• Perishable food e-commerce
• routing
• optimisation
• Internet of Things
• fuzzy logic

Acknowledgements

The authors would like to thank the Research Office of the Hong Kong Polytechnic University, ABC Holdings Limited (alias), and the Hang Seng University of Hong Kong for supporting the project. (Project Code: RUDV).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Y. P. Tsang

Y. P. Tsang is a post-doctoral fellow at the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University. He has finished his Ph.D. study in the summer 2020. He received his Bachelor’s degree in Logistics Engineering and Management from the Hong Kong Polytechnic University in 2015. His current research areas cover IoT applications, blockchain technology, artificial intelligence, cold chain management, and e-commerce services and systems.

C. H. Wu

C. H. Wu received the BEng and PhD degrees in Industrial and Systems Engineering from the Hong Kong Polytechnic University (PolyU) in 2006 and 2011, respectively. He is currently an Assistant Professor of the Department of Supply Chain and Information Management at The Hang Seng University of Hong Kong. He also holds a six-sigma black belt certification from the Hong Kong Society of Quality and he contributes regularly to research papers in the areas of Internet of Things, Engineering Optimisation and Business Intelligence. In collaboration with several scholars at PolyU, Tianjin University, Harbin Institute of Technology, The University of York, etc., his project work and research outcomes have been presented in 10+ international conferences and published in 50+ international refereed journals. As he looks to the future, Dr Wu intends to continue researching in the field of smart logistics and manufacturing.

H. Y. Lam

H. Y. Lam received her BSc (Hons) Logistics Engineering and Management and PhD in Industrial and Systems Engineering from the Hong Kong Polytechnic University in 2008 and 2014, respectively. She is currently a Lecturer in The Hang Seng University of Hong Kong. Her current research areas cover supply chain management, warehouse and logistics management, decision support system and artificial intelligence applications.

K. L. Choy

K. L. Choy gained his MSc degrees in Manufacturing Systems Engineering and in Management Science and his MPhil in Engineering at the University of Warwick, UK in the 1990s and a Doctorate degree at the Hong Kong Polytechnic University in 2003. He is an Associate Professor in the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University. He has published more than 100 international journal papers in the areas of logistics information, data systems, supply chain management and technology management, as well as applying expert systems to industry.

G. T. S. Ho

G. T. S. Ho is currently an Assistant Professor in the Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong. He received his BEng (Hons) in Manufacturing Engineering and PhD in Industrial and Systems Engineering from the Hong Kong Polytechnic University. His research interests range between data mining, artificial intelligent systems, logistics workflow optimisation, logistics and supply chain management. During the years of research, Dr. Ho has published more than 70 papers in both international journals and conference publications.