In this research, two crucial optimization problems of berth allocation and yard assignment in the context of bulk ports are studied. We discuss how these problems are interrelated and can be combined and solved as a single large scale optimization problem. More importantly we highlight the differences in operations between bulk ports and container terminals which highlights the need to devise specific solutions for bulk ports. The objective is to minimize the total service time of vessels berthing at the port. We propose an exact solution algorithm based on a branch and price framework to solve the integrated problem. In the proposed model, the master problem is formulated as a set-partitioning problem, and subproblems to identify columns with negative reduced costs are solved using mixed integer programming. To obtain sub-optimal solutions quickly, a metaheuristic approach based on critical-shaking neighborhood search is presented. The proposed algorithms are tested and validated through numerical experiments based on instances inspired from real bulk port data. The results indicate that the algorithms can be successfully used to solve instances containing up to 40 vessels within reasonable computational time.
In transportation of goods in large container ships, shipping industries need to minimize the time spent at ports to load/unload containers. An optimal stowage of containers on board minimizes unnecessary unloading/reloading movements, while satisfying many operational constraints. We address the basic container stowage planning problem (CSPP). Different heuristics and formulations have been proposed for the CSPP, but finding an optimal stowage plan remains an open problem even for small-sized instances. We introduce a novel formulation that decomposes CSPPs into two sets of decision variables: the first defining how single container stacks evolve over time and the second modeling port-dependent constraints. Its linear relaxation is solved through stabilized column generation and with different heuristic and exact pricing algorithms. The lower bound achieved is then used to find an optimal stowage plan by solving a mixed-integer programming model. The proposed solution method outperforms the methods from the literature and can solve to optimality instances with up to 10 ports and 5,000 containers in a few minutes of computing time.
Seaports consume a large amount of energy and emit greenhouse gas and pollutants. Integrated multiple renewable energy systems constitute a promising approach to reduce the carbon footprint in seaports. However, the intermittent nature of renewable resources, stochastic dynamics of the demand in seaports, and unbalanced structure of seaport energy systems require a proper design of energy storage systems. In this paper, a framework for multi-objective optimization of hybrid energy storage systems in stochastic unbalanced integrated multi-energy systems at sustainable mega seaports is proposed to minimize life-cycle costs and minimize carbon emissions. The optimization problem is formulated with reference to the energy management of the integrated multi-energy system at the seaport and considering both distributed and centralized hybrid energy storage configurations. Wavelet decomposition and double-layer particle swarm optimization are proposed to solve the multi-objective optimization problem. The real power system of the largest port worldwide, i.e., the Ningbo Zhoushan Port, was selected as a case study. The results show that, with respect to a situation with no energy storage system, the proposed approach can save 81.29 million RMB in electricity purchases and eliminate approximately 497,186 tons of carbon emissions over the entire lifecycle of the energy storage system. The findings suggest that the proposed hybrid energy storage framework holds the potential to yield substantial economic and environmental advantages within mega seaports. This framework offers a viable solution for port authorities seeking to implement hybrid energy storage systems aimed at fostering greater sustainability within port operations.
Hydrogen-electricity integrated multi-energy systems are promising approaches to reduce carbon emissions in ports. However, the stochastic nature of renewable energy and the imbalance between the renewable generation and load demand in ports necessitate the design of an appropriate coupled hydrogen-electricity energy storage systems (CHEESS). This paper proposes a multi-objective optimization model for CHEESS configuration in random unbalanced port integrated multi-energy systems (PIMES), aiming to minimize its life-cycle cost and carbon emissions through co-optimization of sizing and energy management. A hierarchical two-stage framework is proposed to solve the multi-objective model. The proposed optimization framework is applied to a real PIMES at the Ningbo-Zhoushan Port. The results show that the proposed method can save 10.54% of the monetary cost and 19.67% of carbon emissions over the entire life-cycle of the system. The study demonstrates that the proposed framework has the potential to generate significant economic and environmental benefits and provides a feasible solution for port authorities seeking to implement CHEESS, aiming to promote sustainability in port operations.
Port Integrated Multi-Energy Systems (PIMES) play a critical role in advancing sustain-ability at ports. Assessing the dynamic contribution of PIMES to port sustainability is essential for guiding future developments. This research introduces an innovative multi-criteria dynamic sustainability assessment framework tailored to evaluate the performance of PIMES. The framework employs a diverse set of indicators covering multiple criteria to comprehensively assess different aspects of PIMES. A game theory-based combined weighting approach is uniquely applied to integrate subjective and objective evaluations, ensuring a balanced and robust assessment. Furthermore, the cloud model is utilized for an in-depth evaluation of the overall sustainability of PIMES, offering a novel perspective on managing uncertainty. The framework's applicability and effectiveness are demonstrated through a case study of the Ningbo-Zhoushan Port, with a sensitivity analysis of the indicators conducted to enhance reliability and confirm the robustness of the proposed method. The evaluation results indicate that during the development of the PIMES, the sustainability performance of the studied port improves progressively, with ratings of “average”, “poor”, “average”, “average”, “good”, and “excellent”. The sensitivity analysis shows that the sustainability of ports is most influenced by the failure loss rate and operation & maintenance cost of PIMES. This framework can serve as a decision-making tool for port authorities to enhance energy efficiency, reduce emissions, and achieve long-term sustainability objectives at ports.
This paper studies the Quay Crane Scheduling Problem (QCSP). The QCSP determines how a number of quay cranes should be scheduled in order to service a vessel with minimum makespan. Previous work considers the QCSP to be a combinatorially hard problem. For that reason, the focus has been on developing efficient heuristics. Our study shows, however, that the QCSP is tractable in the realistic setting, where quay cranes can share the workload of bays. We introduce a novel linear time algorithm that solves the QCSP and prove its correctness.
Due to limited access to domain knowledge and domain-relevant benchmark data, the Container Stowage Planning Problem (CSPP) is notably under-researched. In particular, previous models of the CSPP have lacked two key aspects of the problem: lashing forces and paired block stowage. The former may reduce vessel capacity by up to 10%, and the latter is NP-hard. The Representative CSPP (RCSPP), which captures all critical aspects of the problem is formulated. The presented RCSPP incorporates overlooked constraints such as paired block stowage and lashing, along with an innovative method for estimating lashing forces, all while maintaining simplicity. A heuristic method, STOW, has been developed to identify solutions for the RCSPP using a specially designed benchmark suite based on real-world scenarios. STOW algorithm is an advanced search heuristic employing a diverse range of solution modification strategies, each tailored to address specific aspects of stowage optimization. Feasible solutions were successfully identified for all instances within the benchmark suite. Our initial findings emphasize the importance of accurately modeling lashing forces and employing paired block stowage. Results show that removing the lashing constraint can increase the number of containers stowed by over 7% on average, while disabling paired block stowage can result in nearly a 5% increase.
Waiting times for trucks, trains, airplanes and ships in service represent apparent transport system inefficiencies, and measures to reduce these may have the potential to abate transport GHG emissions. In international shipping, transportation researchers have pointed out that reduced waiting time in association with port calls holds such promise. We explore the potential for GHG abatement through port call optimization, focusing on crews and their employers - the shipping companies. Adding new empirical evidence to the transportation literature, we confirm the existence of idle time during port calls, and go beyond this in describing the causes for it. We show how several port stakeholders, including government officials, limit the crews’ and shipping companies’ room for maneuver in relation to port calls. We also show why the process of reducing waiting time in shipping is more complex than that for onshore transport modes, where real-time traffic information guides drivers’ route choices, and reduces congestion and waiting time. Our findings have implications for both policy makers and transportation research.
This report provides public recommendations for waterborne transport to West Coast of Norway, with the focus on the specified transport system in use case A in the AEGIS project. The complete background for this report is only achieved by reading all the other deliverables related to the use case, however these are confidential. Therefore, it is recommended to read the ICMASS 2022 paper "Development of an advanced, efficient and green intermodal system with autonomous inland and short sea shipping – AEGIS"1 to get a better overview of the project and specifically use case A. The transport system of use case A consists of mother vessel(s) together with one or more daughter vessels, connected by several ports. The mother vessel transports containers from Rotterdam via Hitra Kysthavn and into ports in the Trondheimsfjorden. The rationale for this use case is to enable a more flexible and cost-efficient waterborne transport solution for fjords and smaller ports.
In this paper, we study a problem that integrates the vessel scheduling problem with the berth allocation into a collaborative problem denoted as the multi-port continuous berth allocation problem (MCBAP). This problem optimizes the berth allocation of a set of ships simultaneously in multiple ports while also considering the sailing speed of ships between ports. Due to the highly combinatorial character of the problem, exact methods struggle to scale to large-size instances, which points to exploring heuristic methods. We present a mixed-integer problem formulation for the MCBAP and introduce an adaptive large neighborhood search (ALNS) algorithm enhanced with a local search procedure to solve it. The computational results highlight the method's suitability for larger instances by providing high-quality solutions in short computational times. Practical insights indicate that the carriers’ and terminal operators’ operational costs are impacted in different ways by fuel prices, external ships at port, and the modeling of a continuous quay.