Berth allocation is fundamental to port-related operations in maritime shipping. Port managers have to deal with the increasing demands either by expanding the terminals or by improving efficiency to maintain competitiveness. Port expansion is a long-term project, and it requires much capital investment. Thus, the question of how to enhance the efficiency of berth allocation has received much research interest. Research on the Berth Allocation Problem (BAP) in container ports is quite advanced. However, only limited research focuses on BAP in bulk ports, although some similarities exist. Contributing to Operations Research approaches on the BAP, this paper develops a hybrid BAP mixed-integer optimization model dedicated to bulk ports. In addition to considering the handling characteristics of bulk ports, we also incorporate more practical factors such as unavailability and stock levels. The objective of the proposed model is to minimize the demurrage fee for all vessels under consideration of unavailability and stock constraints. We use the commercial software CPLEX to obtain the optimal solutions for a set of distinct instances, explicitly considering the situation of multiple cargo types on one vessel, which provides a better fit for the loading or discharging operations in real-world bulk ports. This is the first study to our knowledge that dedicates itself to the BAP in bulk ports and considers unavailability and stock constraints simultaneously. Our solutions can provide timely and effective decision support to bulk port managers.@en

Traditionally, terminal operators create an initial berthing plan before the arrival of incoming vessels. This plan involves decisions on when and where to load or discharge containers for the calling vessels. However, disruptive unforeseen events (i.e., arrival delays, equipment breakdowns, tides, or extreme weather) interfere with the implementation of this initial plan. For terminals, berths and quay cranes are both crucial resources, and their capacity limits the efficiency of port operations. Thus, one way to minimize the adverse effects caused by disruption is to ally different terminals to share berthing resources. In some challenging situations, terminal operators also need to consider the extensive transshipment connections between feeder and mother vessels. Therefore, in this work, we investigate a collaborative variant of the berth allocation recovery problem which focuses on the collaboration among terminals and transshipment connections between vessels. We propose a mixed-integer programming model to (re)-optimize the initial berth and quay crane allocation plan and develop a Squeaky Wheel Optimization metaheuristic to find near-optimal solutions for large-scale instances. The results from the performed computational experiments, considering multiple scenarios with disruptive events, show consistent improvements of up to 40% for the suggested collaborative strategy (in terms of costs for the terminal operators).@en

The maritime shipping industry, responsible for 3% of global greenhouse gas (GHG) emissions, is facing increasing pressure to transition towards decarbonization and, ultimately, zero-emission operations, in response to the escalating threat of climate change. To this end, the International Maritime Organization (IMO) has established an ambitious target of reducing 50% of GHG emissions by 2050 (compared with their level in 2008). Green corridors are now recognized as one of the most promising approaches aiming to create zero-emission shipping routes between ports. Significant investment is required to establish the refueling infrastructure supporting alternative-fueled ships, however, and, as of yet, these refueling stations are still undeveloped. Moreover, due to the collaborative nature of the partnership among ports, reasonable distribution of revenue generated from ship refueling is critical for successfully implementing green corridors. Therefore, in this work, we propose the refueling station location problem with green maritime corridors and a general solution framework to assist the government and companies confronted with this problem. We further design a cooperative game to allocate collaborative refueling revenue among involved ports. Specifically, we first develop a flow-refueling location model (FRLM) for the maritime corridor to maximize the ship flow volumes. Then, we develop a row-generation-based algorithm to calculate the core solution for revenue sharing by decomposing the core calculation into the master problem and the subproblem; the output of the subproblem provides a new lower bound for the master problem. To accelerate the computational experiments, we design a metaheuristic to enhance the row-generation algorithm obtaining near-optimal results for the subproblem and more quickly reaching exact core solutions. Depending on the settings of the problem instances, it is possible no sharing strategy can reach the core condition; in these situations, the devised row-generation algorithm can provide lower bounds, defined as the potential revenue for individual port operators, so they are willing to cooperate. Overall, by providing the location of refueling stations and the core allocation of collaborative revenues, this work contributes to establishing maritime green corridors from the operational level.@en