A Predictive–Proactive Approach for Slot Management of a Loading Facility With Truck ETA Information - Prakoso, Emanuel Febrianto; Maknoon, Yousef; Pel, Adam; Tavasszy, Lóránt A.; Vanga, Ratnaji (Frontiers in Future Transportation, 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 congestions and propose to utilize real-time truck location information for improving the performance. We do this by developing an integrated system consisting of a predictive model using Machine Learning (MC) classifiers and an 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 historical traffic data. 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. In the implementation, we first tested multiple ML classifiers and choose the ANN classifier for prediction as it outperformed others. Our limited experiments showed that the proposed method reduced the total rescheduling cost by 42%. Further, our sensitivity analysis based on different levels of congestion, complexity and rescheduling strategy also showed the practicality of the proposed approach.