Integrated inventory control and scheduling decision framework for packaging and products on a reusable transport item sharing platform

Abstract

This study considers the problem of inventory and scheduling decisions on a reusable transport item (RTI) sharing platform with the collaborative recovery of used RTIs and replenishment of products in a two-tier container management centre (CMC). The products (packaged as full RTIs) are pre-positioned at the regional CMC (R-CMC), and empty RTIs are stored at the CMC hub. Moreover, the CMC replenishes the products and recycles RTIs respectively and periodically. The RTI and products are a set of complementary products, and the replenishment task requires sufficient empty RTIs in stock. Untimely and insufficient RTI returns without considering product inventory changes often result in RTI out-of-stock situations that harm the customer's lean productivity. This paper proposes a machine learning and simulation optimisation (MSO) decision framework to collaboratively assist RTI inventory and scheduling decisions in a two-tier CMC. Based on a case study, we can conclude the decision framework has better performance on the profitability and inventory control capability. Moreover, different inventory and scheduling parameter settings in the two-tier CMCs impact the platform's profitability to derive corresponding management insights, and a decision system can be built based on the above framework.

KEYWORDS:

• RTI logistics system
• inventory control and scheduling
• decision support
• RTI sharing platform
• RTI replenishment and recycling

Disclosure statement

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

Data availability statement

Data sharing not applicable – no new data generated.

Additional information

Notes on contributors

Min Guo

Min Guo is a PhD researcher at Nottingham University Business School China and a joint doctoral training student with Shenzhen University. His research interests include operation management of reusable transport items, reverse supply chain, information systems and optimisation.

Xiang T. R. Kong

Dr. Xiang T. R. Kong is Assistant Professor in Supply Chain Management and Dean Assistant of College of Economics, Shenzhen University. His research interests are in Auction Logistics, Mechanism Design and Physical Internet, with research projects at the national and provincial levels.

Hing Kai Chan

Professor Hing Kai Chan has published over 250 academic articles and 9 monographs/edited volumes. His publications appear in Production and Operations Management, European Journal of Operational Research, various IEEE Transactions, Decision Support Systems, International Journal of Production Economics, and International Journal of Production Research, among others. He has been the co-editor of Industrial Management & Data Systems (SCI-indexed) since 2014 and is an Associate Editor of Transportation Research Part E: Logistics and Transportation Review (SCI-indexed) since 2018.

Dimple R. Thadani

Dr. Dimple R. Thadani is currently an Assistant Professor in Information Systems at Nottingham University Business School China. Dimple received her PhD from City University of Hong Kong. Her research interests include social media, leadership and online collaborative games, e-commerce, and e-learning. She has published in international journals and leading information systems conference proceedings.