A Port Digital Twin Model for Operational Uncertainty Management

Authors

  • Siraprapa Wattanakul 1. University of Lyon, University Lyon 2, DISP Laboratory, France 2. College of Arts, Media and Technology, Chiang Mai University, Thailand
  • Sébastien Henry University of Lyon, University Lyon 1, DISP Laboratory, France
  • Napaporn Reeveerakul College of Arts, Media and Technology, Chiang Mai University, Thailand
  • Yacine Ouzrout University of Lyon, University Lyon 2, DISP Laboratory, France

DOI:

https://doi.org/10.19255/JMPM02810

Keywords:

Digital Twin, Uncertainty Management, Port Operation, Berth Allocation, Reactivity

Abstract

Lacking information challenges the managementof port operational uncertainty in estimating the situation tosupport a decision on reactivity planning. This paper applies Digital Twin (DT) to model a replicated virtual port operationfrom a real-world port of Thailand. The proposed DT modeloffers a tool to accelerate generating data of the port operation with configurable uncertainty. The model is validated by using generated data from the DT model compared with the real-world data. The result shows that the DT model produces the same behaviour as the real-world system. An outcome of this paper is a DT model eligible to generate port operation data for later applying with a machine learning to predict the port capacity under uncertainty to support reactivity planning.

Author Biographies

Siraprapa Wattanakul, 1. University of Lyon, University Lyon 2, DISP Laboratory, France 2. College of Arts, Media and Technology, Chiang Mai University, Thailand

Siraprapa Wattanakul is a PhD candidate in a cotutelle program at the DISP Laboratory, University Lumiere Lyon2 and Chiang Mai University supported by Erasmus-Mundus SmartLink. She is doing PhD in the field of Computer Science and Knowledge Management. Currently, she is a lecturer in Software Engineering at Chiang Mai University. Her research includes data and knowledge management, simulation, machine learning and decision support system.

Sébastien Henry, University of Lyon, University Lyon 1, DISP Laboratory, France

Dr. Sébastien Henry is a Computer Scientist in Industry 4.0 group of the DISP Laboratory at the University Lumiere Lyon1. He obtained his PhD in Computer Science from the Grenoble-INP in 2005. Currently associate professor at University of Lyon 1, he is head of mechanical department of the Institute of Technology of his university (IUT Lyon 1) and co-leader of the ”Information System and Data” research team of DISP Lab. His main research topics are data management, process assessment and decision making based on machine learning and model-based approaches in the fields of energy, food, mechanics, etc.

Napaporn Reeveerakul, College of Arts, Media and Technology, Chiang Mai University, Thailand

Asst.Prof.Dr.Napaporn Reeveerakul is a lecturer in Modern Management and Information Technology, Knowledge Innovative Management at College of Arts, Media and Technology(CAMT), Chiang Mai University(CMU),Thailand. Currently, she is also a director of Digital Innovative and Technology Center, CAMT, CMU.(https://ditc.camt.cmu.ac.th/). Prior to this, she was a cotutelle PhD in a research domain of Production and Informatique which is jointly enrolled at Chiang Mai University and Université Lumière Lyon. Her experiences and professional skills are Decision Making , Supply Chain Management Tools for Improvement, Simulation and Knowledge Management. She was a coordinator of International Projects:

Yacine Ouzrout, University of Lyon, University Lyon 2, DISP Laboratory, France

Dr Yacine Ouzrout is a Computer Scientist in the Supply Chain & Product Lifecycle Management group of the DISP Laboratory at the University Lumiere Lyon2. He obtained his PhD in Computer Science from the INSA Lyon, and his HDR Diploma in 2012 from the University Lyon 2. Currently, he is Professor and the Director of the Institute of Technology of his university. He is Deputy Director of the DISP Laboratory, and his research interests include multi-agent systems, knowledge management,  simulation, decision support systems, and distributed information systems. Pr. Ouzrout has been involved in several European projects: Asia-Link East-West, Erasmus-Mundus eLink, cLink, eTourism, Fusion, Smartlink… FP7 Fitman and Easy-IMP and H2020 vf-OS and DIH4CPS. He is currently the coordinator of an Erasmus+ project SHYFTE 4.0. He is a member of the IFIP WG5.1 about “Global Product development for the whole life-cycle". He is co-chair and member of program committees & reviewer of several international conferences and Journals. He has graduated 17 doctorates (four of them in collaboration with international Universities) and over 40 Master students.

References

Bierwirth, C., & Meisel, F. (2010, may). A survey of berth allocation and quay crane scheduling problems in container terminals. European Journal of Operational Research, 202(3), 615–627. Retrieved from https://www.sciencedirect.com/science/article/pii/S0377221709003579 doi:10.1016/J.EJOR.2009.05.031

Burns, M. G. (2018). Port management and operations. doi:10.4324/9781315275215

Cahyono, R. T., Flonk, E. J., & Jayawardhana, B. (2020, mar). Discrete-event systems modelling and the model predictive allocation algorithm for integrated berth and quay crane allocation. IEEE Transactions on Intelligent Transportation Systems, 21(3), 1321–1331. doi: 10.1109/TITS.2019.2910283

Ganin, A. A., Massaro, E., Gutfraind, A., Steen, N., Keisler, J. M., Kott, A., . . . Linkov, I. (2016, jan). Operational resilience: concepts, design and analysis. Scientific Reports 2016 6:1, 6(1), 1–12. Retrieved from https://www.nature.com/articles/srep19540 doi: 10.1038/srep19540

Grieves, M., & Vickers, J. (2017, jan). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, 85–113. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-38756-7 4 doi:

/978-3-319-38756-7 4

Hofmann, W., & Branding, F. (2019, sep). Implementation of an IoT- And cloud-based digital twin for real-time decision support in port operations. IFAC-PapersOnLine, 52(13), 2104–2109. doi: 10.1016/J.IFACOL.2019.11.516

Hossain, N. U. I., Nur, F., Hosseini, S., Jaradat, R., Marufuzzaman, M., & Puryear, S. M. (2019, sep). A Bayesian network based approach for modelling and assessing resilience A case study of a full service deep water port. Reliability Engineering I& System Safety, 189, 378–396. doi: 10.1016/J.RESS.2019.04.037

Huang, J., Wang, F., & Shi, N. (2014). Resource allocation problems in port operations: A literaturerevie w. In Proceedings - 2014 7th international joint conference on computational sciences and optimization, cso 2014 (pp. 154–158). doi: 10.1109/

CSO.2014.35

Iris, C¸ ., & Lam, J. (2019). Recoverable robustness in weekly berth and quay crane planning. Transportation Research Part B: Methodological, 122, 365–389. doi: 10.1016/j.trb.2019.02.013

Liming, G., Jun, W., & Jianfeng, Z. (2021, aug). Berth allocation problem with uncertain vessel handling times considering weather conditions. Computers and Industrial Engineering, 158. Retrieved from https://dl.acm.org/doi/abs/10.1016/j.cie.2021.107417 doi: 10.1016/J.CIE.2021.107417

Notteboom, T., Pallis, A. A., & Rodrigue, J.-P. (2022). Port economics, management and policy. Retrieved from https://porteconomicsmanagement.org/pemp/contents/part6/

Russell, D., Ruamsook, K., & Roso, V. (2020). Managing supply chain uncertainty by building flexibility in container port capacity: a logistics triad perspective and the COVID-19 case. Maritime Economics and Logistics. Retrieved from https://doi

.org/10.1057/s41278-020-00168-1 doi:10.1057/s41278-020-00168-1

The Loadstar. (2020a). Congestion problems at UK ports stacking up as rising imports drive delays. Retrieved 2020-12-08, from https://theloadstar.com/congestion-problems-at-uk-ports-stacking-up-as-rising-imports-drive-delays/

The Loadstar. (2020b). Peak season and port congestion surcharges spread to Asian tradelanes. Retrieved 2020-12-08, from https://theloadstar.com/peak-season-and-port-congestion-surcharges-spread-to-asian-tradelanes/

Umang, N., Bierlaire, M., & Erera, A. (2017). Real-time management of berth allocation with stochastic arrival and handling times. Journal of Scheduling,20(1), 67–83. doi: 10.1007/s10951-016-0480-2

UNCTAD. (2020). Review of maritime transport:2020 (S. N. Sirimanne, J. Hoffmann, & W. Juan, Eds.). New York: United Nations Publications. Retrieved from https://unctad.org/system/files/official-document/rmt2020 en.pdf

Vugrin, E. D., Warren, D. E., & Ehlen, M. A. (2011, sep). A resilience assessment framework for infrastructure and economic systems: Quantitative and qualitative resilience analysis of petrochemical supply chains to a hurricane. Process Safety Progress, 30(3), 280–290. Retrieved from https://aiche.onlinelibrary.wiley.com/doi/10.1002/prs.10437 doi:10.1002/PRS.10437

Xiang, X., & Liu, C. (2021). An expanded robust optimisation approach for the berth allocation problem considering uncertain operation time. Omega (United Kingdom). doi: 10.1016/j.omega.2021.102444

Xiang, X., Liu, C., & Miao, L. (2018, dec). Reactive strategy for discrete berth allocation and quay crane assignment problems under uncertainty. Computers & Industrial Engineering, 126, 196–216. doi:10.1016/J.CIE.2018.09.033

Yu, H., Ning, J., Wang, Y., He, J., & Tan, C. (2021, oct). Flexible yard management in container terminals for uncertain retrieving sequence. Ocean & Coastal Management, 212, 105794. doi: 10.1016/J.OCECOAMAN.2021.105794

Yu, J., Tang, G., Song, X., Yu, X., Qi, Y., Li, D., & Zhang, Y. (2018, jun). Ship arrival prediction and its value on daily container terminal operation. Ocean Engineering, 157, 73–86. doi: 10.1016/j.oceaneng.2018.03.038

Zhen, L. (2015). Tactical berth allocation under uncertainty. European Journal of Operational Research, 247(3), 928–944. doi: 10.1016/j.ejor.2015.05.079

Zhou, C., Xu, J., Miller-Hooks, E., Zhou, W., Chen, C. H., Lee, L. H., . . . Li, H. (2021, apr). Analytics with digital-twinning: A decision support system for maintaining a resilient port. Decision Support Systems, 143, 113496. doi: 10.1016/J.DSS.2021.113496

Downloads

Published

2022-05-20

How to Cite

Wattanakul, S., Henry, S., Reeveerakul, N., & Ouzrout, Y. (2022). A Port Digital Twin Model for Operational Uncertainty Management. The Journal of Modern Project Management, 9(3). https://doi.org/10.19255/JMPM02810