Data-driven Game-theoretic Model Based on Blockchain for Managing Resource Allocation and Vehicle Routing in Modular Integrated Construction

ABSTRACT

In modular integrated construction (MiC), the resource allocation problem (RAP) adopted by construction sites and vehicle routing problem (VRP) adopted by logistics companies are highly interdependent. However, this interdependence has been overlooked in the literature. Thus, the plans determined by each problem cannot be achieved practically. Moreover, there is no existing VRP model suitable for the MiC application. This study aims to propose a VRP model suitable for MiC, investigate the interdependence between the RAP and proposed VRP model, and develop a blockchain system to secure information sharing between the RAP and VRP. The first two objectives are achieved by developing a coordinated system formulated as a leader-follower Stackelberg game model (LFSGM). This model is presented as a bi-level optimisation model and solved using a nested ant colony optimisation-based algorithm. The effectiveness of the LFSGM is validated using a real case study. The results show that using the traditional approach (i.e. a separate RAP and proposed VRP) succeeds in providing a plan for the construction sites but not for the logistics company. In contrast, the LFSGM offers applicable plans for both the construction sites and logistics company. Lastly, some managerial implications are identified and discussed.

KEYWORDS:

• Resource allocation problem
• vehicle routing problem
• game theory
• blockchain
• modular integrated construction

Acknowledgement

The study presented in this article was mainly supported by a grant from the Research Committee of The Hong Kong Polytechnic University under project code P0036181 and RGC (Hong Kong).

Disclosure statement

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

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article. Any other data will be available upon reasonable request.

Notes

1 https://www.mckinsey.com/∼/media/mckinsey/business%20functions/operations/our%20insights/voices%20on%20infrastructure%20scaling%20modular%20construction/gii-voices-sept-2019.pdf

Additional information

Funding

This work was supported by The University Grant Committee of Hong Kong Polytechnic University: [Grant Number Project No. P0036181].

Notes on contributors

Abdelrahman E.E. Eltoukhy

Abdelrahman E.E. Eltoukhy graduated with a B.Sc. degree in Production Engineering from Helwan University, Egypt, and received his M.Sc. degree in Engineering and Management from Politecnico Di Torino, Italy. He received his Ph.D. degree from The Hong Kong Polytechnic University, Hong Kong. Before joining The Hong Kong Polytechnic University, he worked as an Assistant Professor in the Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. Later, he moved to the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, as a Research Assistant Professor. His current research interests include Airline Schedule Planning, Logistics and Supply Chain Management, Operations Research, Simulation, AI Optimization, and Robot Localization and Mapping.

Mohamed Hussein

Mohamed Hussein is currently a Ph.D. candidate in the Building and Real Estate Department at the Hong Kong Polytechnic University and an assistant lecturer at the Civil Engineering Department, Faculty of Engineering, Assiut University, Egypt. He received his B.Sc. and M.Sc. degrees in Civil Engineering from Assiut University, Egypt. His current research interests include: Off-site Construction Management, Modular integrated Construction, Logistics and Supply Chain Management, Water Distribution Networks, Simulation, Optimization, and Blockchain.

Min Xu

Min Xu is currently an Assistant Professor in the Industrial and Systems Engineering Department at Hong Kong Polytechnic University. She received Ph.D. degree in Transportation Engineering from the National University of Singapore, Singapore and a double bachelor’s degree in Engineering and Economics from Tsinghua University. Her research focuses on using optimisation models and algorithms to design and develop operation strategies for shared mobility and logistics systems with electric vehicles and connected and autonomous vehicles, transportation network modelling, travellers’ behaviour modelling, and public transit modelling and optimisation. She serves on the editorial board of Transportation Research Part C and Transportation Research Part E.

Felix T.S. Chan

Felix T.S. Chan received his BSc Degree in Mechanical Engineering from Brighton University, UK, and obtained his MSc and PhD in Manufacturing Engineering from the Imperial College of Science and Technology, University of London, UK. Prior joining Macau University of Science and Technology, Prof. Chan has many years of working experience in other universities, including The Hong Kong Polytechnic University; University of Hong Kong; University of South Australia; University of Strathclyde. His current research interests are Logistics and Supply Chain Management, Decision Making, AI Optimisation, Operations Research, Production and Operations Management, Distribution Coordination. Prof. Chan has published over 16 book chapters, over 390 SCI refereed international journal papers, and 320 peer reviewed international conference papers. His total number of citations > 11000, h Index =  54. Prof. Chan is a chartered member of the Chartered Institute of Logistics and Transport in Hong Kong.

Based on the recent compilations (2020) and (2021) from a research group of Stanford about the impact of scientists (top 2% listed). The work is published in the following websites: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000918; and doi: 10.17632/btchxktzyw.3. Prof. Felix Chan is categorised in the field of Operations Research, ranked 10 out of over 23,450 scientists worldwide, i.e. Top 0.04% worldwide, for TWO consecutive years (2020 and 2021).