From container terminals to bulk ports: models and algorithms for integrated planning and robust scheduling

In the operations research (OR) literature on port operations planning, there are a significant number of studies addressing decision problems in the context of container terminal management. Bulk terminals on the other hand, have been largely ignored. In this thesis, we study some of the key decision problems such as the berth allocation problem and the yard assignment problem in the bulk context. The berth allocation problem (BAP) in bulk ports differs from that in container terminals, primarily because it is necessary to explicitly account for the cargo type on the vessel and the locations of the fixed equipment facilities such as conveyors and pipelines that are installed at only certain sections along the quay. We develop exact and heuristic algorithms to solve the BAP in bulk ports. The results based on instances inspired from real bulk port data look promising and suggest that the proposed methods can be successfully used to improve the operational efficiency of berth scheduling in bulk ports. The BAP model is later extended and solved in integration with the yard assignment problem, that is, the problem of assigning different cargo types to specific locations in the yard. We propose a sophisticated exact solution algorithm based on the branch-and-price framework to solve the combined problem of berth allocation and yard assignment, which in all the previous studies related to container terminals has been solved using metaheuristics. Computational results based on real bulk port data suggest that the proposed algorithm can be successfully used to solve realistic sized instances in a computational time that is reasonable enough for the algorithm to be actually implemented and put into practice at the port. Another key challenge in port operations planning is to address the enormous amount of uncertainty on account of factors such as weather conditions, mechanical problems and labor inefficiency among others. A stochastic disturbance can possibly render the planned schedules infeasible, thus incurring high costs to the port. In the current literature, there are very few studies related to handling uncertainty in port operations. In this thesis, we propose innovative models and solution techniques to handle uncertainty in scheduling, based on