A Predictive–Proactive Approach for Slot Management of a Loading Facility With Truck ETA Information

A Predictive–Proactive Approach for Slot Management of a Loading Facility With Truck ETA Information

Prakoso, Emanuel Febrianto (Student TU Delft)
Maknoon, M.Y. (TU Delft Transport and Logistics)
Pel, A.J. (TU Delft Transport and Planning)
Tavasszy, Lorant (TU Delft Transport and Planning; TU Delft Transport and Logistics)
Vanga, R. (TU Delft Transport and Logistics)

2022

Due to the uncertain and dynamic environment around scheduling systems, timely revisions or reschedules of the master plans are essential for achieving optimal utilization. With the recent development of Industry 4.0 technologies, many researchers perceive the creation of cyber-physical systems as a solution for managing systems under uncertainty. This article focuses on a loading facility under uncertain truck arrivals due to road congestion and proposes utilizing real-time truck location information to improve performance. We do this by developing an integrated system consisting of a predictive model using machine learning (MC) classifiers and a mathematical model for real-time slot rescheduling. The ML classifier is used to predict the presence probabilities of all the incoming trucks at a particular slot based on the historical traffic data and the real-time truck location. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) model is developed to solve a Probabilistic Slot Rescheduling Problem (P-SRP), which uses the estimated truck presence probabilities and minimizes the total expected cost of rescheduling. We implemented this by first testing multiple ML classifiers and selected the ANN classifier for prediction as it outperformed others. Our limited experiments showed that the proposed method reduced the total rescheduling cost by 42%. Furthermore, our sensitivity analysis with different congestion levels, complexity, and rescheduling strategy also showed the practicality of the proposed approach.

real-time ETA information
proactive rescheduling
truck ETA
probabilistic optimization
machine learning
uncertain arrivals

http://resolver.tudelft.nl/uuid:59e71a1a-6ecb-432f-b292-84d9dd485a1b

https://doi.org/10.3389/ffutr.2022.815267

2673-5210

Frontiers in Future Transportation, 3 (4), 1-16

Institutional Repository

journal article

© 2022 Emanuel Febrianto Prakoso, M.Y. Maknoon, A.J. Pel, Lorant Tavasszy, R. Vanga