Long truck queues and congestion around terminals is a common sight, however they come with many negative externalities for all the stakeholders involved. Truck Appointment System (TAS) is the most commonly used system to face these problems, but it still has some drawbacks and limitations. Consequently, in this research an extension of the typical TAS is proposed in order to improve its performance. The main components of this system are the use of truck dependent time-windows, the utilization of real-time information and the adaptive trucks rescheduling model. The duration of the arrival time-windows is longer than the actual service times, allowing overlap between time-windows. Thus, the actual service sequence might be different from the reserved one. To determine the actual loading sequence an Optimization model is developed, which is run periodically while utilizing real-time truck information. A chemical plant is used as a case study in this research. The performance of the proposed TAS is assessed with the use of a Simulation model. The outcomes of this research suggest that the a less strict TAS can significantly improve the system’s performance, especially trucks’ waiting times. Also, the system’s resilience against disruptions and the plant’s environmental footprint are improved, while queues are reduced.

This study addresses the truck rescheduling problem as the consequence of uncertain arrival time. It proposes an integrated system of predictive model powered by machine learning algorithm and exact optimization model such that it is distinct from most existing literatures in this domain. The uncertainty of truck arrival time is captured as presence probability by the developed predictive model. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) is built to solve the Probabilistic Slot Rescheduling Problem (P-SRP) in which the rescheduling is subject to expected value constraint that incorporates the presence probability of incoming trucks. The objective is to minimize the expected cost of rescheduling that would lead to more efficient and robust operation. In regard to the predictive model, evaluation according to the standardized KPI shows the ANN is the best algorithm to fit the input historical data with overall F1 score of 73%. Moreover, adding real-time elements enhances the prediction result by 20%. In regard to optimization model, the P-SRP model results on expected cost that is 42% lower than the P-SRP model. Numerical experiment based on multiple scenarios indicates that the proposed solution yields optimal added values in situation characterized by high number of reschedule being required, large scale of operation in terms of quantity of loading bay and utilization rate, the priority is to maintain initial schedule, and there is no stand-in loading bay available. Lastly, it is found that increasing operational efficiency comes at the expense of the schedule robustness, although the value is negligible. This study is limited to the conceptual setting and the use of synthetic data; therefore it should be extended to include empirical result.