Scheduling wagons to unload in bulk cargo ports with uncertain processing times

Optimising operations in bulk cargo ports is of great relevance due to their major participation in international trade. In inbound operations, which are critical to meet due dates, the product typically arrives by train and must be transferred to the stockyard. This process requires several machines and is subject to frequent disruptions leading to uncertain processing times. This work focuses on the scheduling problem of unloading the wagons to the stockyard, approaching both the deterministic and the stochastic versions. For the deterministic problem, we compare three solution approaches: a Mixed Integer Programming model, a Constraint Programming model and a Greedy Randomised algorithm. The selection rule of the latter is evolved by Genetic Programming. The stochastic version is tackled by dispatching rules, also evolved via Genetic Programming. The proposed approaches are validated using real data from a leading company in the mining sector. Results show that the new heuristic presents similar results to the company's algorithm in a considerably shorter computational time. Moreover, we perform extensive computational experiments to validate the methods on a wide spectrum of randomly generated instances. Finally, as managing uncertainty is fundamental for the effectiveness of these operations, distinct strategies are compared, ranging from purely predictive to completely reactive scheduling. We conclude that re-scheduling with high frequency is the best approach to avoid performance deterioration under schedule disruptions, and using the evolved dispatching rules incur fewer deviations from the original schedule.

Automated summary

Optimizing operations in bulk cargo ports is crucial due to their importance in international trade. This study focuses on the scheduling problem of unloading wagons in the stockyard, addressing both the deterministic and stochastic versions. Various solution approaches, including Mixed Integer Programming, Constraint Programming, and Genetic Programming, are compared and validated using real data from a leading mining company. The results show that the new heuristic algorithm achieves similar performance to the existing algorithm with significantly reduced computational time. Additionally, the study compares different scheduling strategies and concludes that frequent re-scheduling is the most effective approach in dealing with disruptions while evolved dispatching rules result in fewer deviations from the original schedule.