Accounting for about 80% of world trade by volume and over 70% of world trade by value, maritime transport plays a central role in international trade and logistics. Over 10.7 billion tons of goods were transported by sea in 2018 and global maritime trade is expected to grow at an average annual rate of 3.5% until 2024. Ports are key components of maritime trade and hence global economy, linking maritime trade routes to each other and other segments of transport systems. Thus, optimizing port operations can greatly facilitate the transfer of goods and reduce delays in delivery to destinations. Most manufactured goods are transported in standard containers, which can be easily loaded on and off vessels with special equipment. One of the most important performance criteria for container terminals is the time it takes for vessels to load/unload their cargo in these terminals. Given the magnitude of the cost imposed each day a vessel remains docked in a port, port authorities and shipping companies try to minimize this time. This can be done by optimizing the plan and schedule of seaside operations, i.e. how arriving vessels dock in the quay and how they are served by quay cranes. This research presents, for the first time, a berthing management approach called berthing preemption, which can reduce berthing, waiting, and idle times and their consequent costs. This approach increases the flexibility of seaside operations planning by allowing loading/unloading operations of a vessel to be conducted in multiple sessions separated by departure from and reentry to the quay. In this thesis, two problems in the field of seaside operations planning are proposed. The first problem was the development of an integrated model for berth allocation and quay crane assignment and scheduling in continuous container terminals where berthing possibility depends on water depth and tide conditions. This model was also equipped with several constraints that ensure quay cranes keep a safe distance from each other and do not cross over each other. A Random-Topology Particle Swarm Optimization algorithm (RTPSO) was also developed for solving the large instances of this problem. For performance eva‎luation, the results of the developed algorithm were compared with the results of the exact solution and Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms. In this eva‎luation, the best solutions of SA, PSO, and RTPSO for large-sized problem instances had on average 2.4%, 1.3%, and 0.3% deviation from the best possible solutions, which demonstrated the superior performance of RTPSO in solving this problem. The second problem was the introduction of the concept of berthing preemption to seaside operations planning, which was done by formulating this assumption into a mixed-integer model for integrated berth allocation and quay crane assignment and scheduling in continuous container terminals. An Accelerated Benders’ Decomposition Algorithm (ABDA) was also developed to solve the instances of this problem. In the performance eva‎luation, the best solutions of ABDA and Guroby solver for instances of this problem were found to have on average 3.25% and 12.22% deviation from the best possible solutions, which showed the better performance of the developed ABDA in solving problem instances of different sizes.

مهدي علينقيان

حجازي، رضا

قاسم مصلحي، علي بزرگي اميري، امير عباس نجفي