In Supply Chains (SC), loading facilities play a crucial role in facilitating the loading/unloading activities at different SC terminals, such as production facilities, warehouses, cross-docks, railways, and seaports. The efficiency of their initial plan is affected by road congestions and associated truck arrivals at loading terminals. Late truck arrival requires rescheduling to find a new slot for the late truck, that may affect the delivery time to the customer. It means that traffic congestion is uncertain and undesirable in logistics and leads to arrival uncertainty at downstream locations, engendering disruptions.
This paper considers a loading facility that uses a Truck Appointment System (TAS) for slot management and faces incoming truck arrival uncertainty due to traffic congestion. Due to the recent advancements in cyber-physical systems, the developers proposed an adaptive system that used the real-time truck Estimated Time of Arrival (ETA) data to make informed decisions. They developed an integer mathematical model to represent the adaptive behavior that determines the optimal reschedules by minimizing the average truck waiting time.
The model was intended to emulate the adaptive system. It was created in AnyLogic simulation software based on the conceptual model. The rescheduling formulation was modeled in the Gurobi Java interface and solved using the Gurobi optimization solver. There, the researchers developed the necessary integration of the mathematical model using the custom function element in AnyLogic software.
As a result, they could get system representation in a digital environment and report the estimated benefits from our initial experiments. The results proved that the availability of ETA information would reduce the average truck waiting time by 20% and improve all KPIs, and the next steps in this research include conducting a sensitivity analysis of the proposed system against changes in demand and congestion levels.
Process flow diagram of loading operations