The globalization of trade and subsequent growth of containerization for transporting goods in containers have brought many challenges for marine terminals. Increasing demand, capacity constraints, lack of adequate decision making tools, congestion and environmental concerns are some of the major issues faced by the marine terminals today. Such terminals involve various processes in their operations and effective decision making is imperative in each process to manage scarce resources and improve the terminals’ competitiveness. This thesis consists of significant research work conducted in two critical areas of marine terminal operations. Specifically, it addresses the yard crane scheduling problem and truck queuing problem at terminal gates. Both of these problems share common objectives such as minimizing turn time of drayage trucks, reducing congestion and emission, and enhancing productivity of the terminals.

It is recognized that better yard crane scheduling could result in less waiting time for drayage trucks. The first study presents a comparative study of two contrasting approaches for modeling the yard crane scheduling problem: centralized and decentralized. It seeks to assess their relative performances and generate insights on their disparate features. Our analysis shows that the centralized approach outperforms the decentralized approach by 16.5% on average, due to having complete and accurate information about future truck arrivals. While it underperforms the centralized, the decentralized approach can dynamically adapt to real-time truck arrivals, making it better suited for real-life operations. Overall, our analysis suggests that the two approaches offer complementary features that could be integrated into a hybrid approach.

Queuing at marine terminal gates has long been identified as a source of emissions and high drayage costs due to the large number of trucks idling. The second study in this thesis addresses queuing of trucks at marine terminal gates and presents a novel agent-based framework where the drayage companies can minimize congestion by using the provided real-time gate queuing information. The problem was tackled based on the approach of El Farol Bar problem from game theory. Our proposed model can be used as a means of managing demand for the marine terminals, assuming that drayage firms will adjust their plans based on the real-time feedback of congestion. Results from our extensive experiments suggest that the proposed multi-agent framework can produce more steady truck arrivals at terminal gates and therefore significantly less average waiting time.