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.
The shipping industry's greenhouse gas emission reduction has received significant focus over the past years. One of the research areas is that of stowage planning for RORO vessels. Efficient stowage plans are necessary to reduce the turnaround time for vessels in a port. Reducing turnaround time results in prolonged sailing time, allowing vessels to reduce fuel consumption through slow steaming. When RORO vessels have calls at several ports, they handle cargo as an approximate FILO queue. Therefore, cargo can potentially become blocked when stowing cargo for later ports, behind cargo with an earlier discharge port. Planning the cargo assignment onboard the vessels also requires considering the arrival time of cargo at the port. Recent research assumes that all freight is available for stowage when the RORO vessels arrive at the port. However, this is not always the case. The unique elements of scheduling and generation of loading/discharge paths are therefore of academic interest. We propose a novel mathematical model with a weighted objective function that minimizes the relationship between the fuel consumption cost and the revenue gained from shipping cargo. The model schedules the cargo loading sequence to reduce time spent handling and re-handling cargo at each port. The problem is studied for a single deck layout for a vessel calling multiple ports. Results of the mathematical model and accompanying metaheuristic will be presented.
Modern ports face not only a paradox of combining efficiency and effectiveness, but also a paradox of balancing activities characterized by different time horizons and stakeholder expectations. The structural changes underlying these paradoxes are the co-existence of downward pressures on market premiums and the increasing demands on the relational capabilities of port authorities. The increasing demand on relational capabilities is caused by the fact that modern ports are hubs for industrial activities that span the organizational boundaries of firms, integrating port systems and the hinterland. Thus, port authorities must simultaneously focus on cost efficiency and systemic coordination within complex port systems. As indicated by recent research on port governance and competitiveness, this implies that port authorities must assume and combine different organizational roles. The present paper takes this discussion further by classifying the organizational roles of port authorities in terms of role complexity, relational capital, and systemic functions within the port system. Based on a case study, the paper shows that the use of systemic functions implies the development of new business models, and that the adoption of the roles by port authorities depends on how emerging relational capabilities are embedded in structures of value co-creation and value co-capture.
The maritime sector contributes significantly to climate change, given thenumber of global emissions that this represents. Emissions inventorying isone of the measurement system approaches considered in terminals to mitigate harmful emissions. The concept of sustainability has gained attentionwhere economic, social, and environmental dimensions need to be balanced.Assessing all three sustainability dimensions is important. Both the environment and the society, e.g., human health and safety, are impacted by terminal operations. Reducing their negative impact can compromise the economicgrowth of the terminal. This is challenging the maritime sector, and althoughsome authors define methods to evaluate sustainability in terminals, nostandard guideline is available in the literature. The lack of a common reference guideline makes comparison of sustainability actions in terminals difficult.This paper presents a sustainability assessment framework based on theanalysis of the state of the art in literature contributing to sustainable development of terminals and supporting decision-makers.
Roll-on roll-off (RoRo) shipping plays an important role in freight transport on the European continent, and is faced with the challenge of reducing its CO2 emissions while increasing its efficiency. Dual cycling, in which loading and discharging processes are carried out simultaneously, achieves this goal by reducing the turnaround time of vessels in port and thus the CO2 output of handling equipment in port and fuel consumption through slow steaming at sea. Optimizing the dual cycling operations on RoRo vessels has not yet been investigated in the literature. This paper presents the novel RoRo dual cycling problem (RRDCP), and formulates it using integer programming (IP) with the objective to minimize the total makespan of discharging and loading operations. We further prove that the RRDCP is NP-complete by a reduction from a general machine scheduling problem, and introduce a novel heuristic to solve the problem called a generalized random key algorithm (GRKA). We evaluate the IP model and GRKA approach on both generated and industrial instances, showing that the GRKA heuristic finds optimal or near-optimal solutions to real-world problems in just seconds. We provide managerial insights on industrial instances, which indicate that our approach leads to a reduction in fuel consumption and CO2 emissions of up to 25% for RoRo operations.
We consider a variant of the berth allocation problem-i.e., the multi-port berth allocation problem-aimed at assigning berthing times and positions to vessels in container terminals. This variant involves optimizing vessel travel speeds between multiple ports, thereby exploiting the potentials of a collaboration between carriers (shipping lines) and terminal operators. Using a graph representation of the problem, we reformulate an existing mixed-integer problem into a generalized set partitioning problem, in which each variable refers to a sequence of feasible berths in the ports that the vessel visits. By integrating column generation and cut separation in a branch-and-cut-and-price procedure, our proposed method is able to outperform commercial solvers in a set of benchmark instances and adapt better to larger instances. In addition, we apply cooperative game theory methods to efficiently distribute the savings resulting from a potential collaboration and show that both carriers and terminal operators would benefit from collaborating.
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.
This report includes a broad description of the findings from work package 2 in the EFFORT project and is made as the fulfillment of delivery L2.1 in the project. First an overall description of the Port of Hirtshals together with its infrastructure is given in chapter 1 together with some background aspect for the development of the Port of Hirtshals. In this chapter also the 5 companies who had shown their interest in participation in the project are described in more detail. Based on this as outcome of task 2.1 and described in chapter 2 an overall system architecture is set up for the existing industries at the Port of Hirtshals and next for the future expansion of the port. Based on the overall system architecture an adaptation of the system to the EU SGAM model is performed and explained. Then the overall set up of the data hub is briefly introduced, to see how it is related to the overall energy system set up. The final part documented for task 2.1 is two examples of sequence diagrams for first the processes in Forskerparken and next one which is valid for both the Fish Terminal, Lineage as well as Danish Salmon, since many of their electrical consuming processes here in an overall manner look the same.
In chapter 3 the base scenarios for the existing industries at Port of Hirtshals are set up. This is done based on information and wishes from the industries and the local Distribution System Operator (DSO), which is gained partly by bilateral discussions as well as on a workshop held with all the involved industries present at the same time. The scenarios will be described according to the IEC standard 62559-2, to ensure better utilization of the ideas in other projects, by applying a standard template known in this area.
Finally, in chapter 4 scenarios for the future expected extension of industries and activities at the Port of Hirtshals are set up. This is based on inputs from GPN, HH, NEN as well as Hjørring Municipality, Hirtshals Fjernvarme and from inputs from workshops with the existing industries at the port. Also here the IEC 62559-2 standard will be applied when describing the use cases.
The scenarios set up will later be used for the further development of the data hub, which is to be set up in the project, as well as for the model set up and control perspectives in the later WPs.
In the present work, the determinants of port choice regarding container cargoes from specific hinterland regions are analyzed, based on an empirical study of Spain. Previous work has been extended by including novel explanatory variables for the market shares of ports in hinterland locations. Discrete choice theory is the methodological approach used here. More specifically, a nested logit model is proposed. As potential explanatory variables, the model includes maritime connectivity to specific overseas regions and intermodal connectivity of the port to specific hinterland locations. The empirical analysis is based on detailed Spanish customs data. The analysis shows that all variables hypothesized to influence the market share of a port in a specific hinterland region (i.e., road distance to the hinterland region, maritime distance, maritime connectivity of the port, and intermodal connectivity of the port) indeed influence significantly its market share, with the signs as expected. The findings add to the understanding of port competitiveness in specific regions with three conclusions: First, port hinterlands are relational, in the sense that they depend on the overseas origin or destination of the cargo; Second, the analysis suggests that ports that predominantly handle transhipment cargoes may have a “transhipment orientation,” which is an impediment for reaching hinterland markets; Third, intermodal connectivity is a determinant of the market share of a port in a certain hinterland region.
Due to increasing container traffic and mega-ships, many seaports face challenges of huge amounts of truck arrivals and congestion problem at terminal gates, which affect port efficiency and generate serious air pollution. To solve this congestion problem, we propose a solution of managing truck arrivals with time windows based on the truck-vessel service relationship, specifically trucks delivering containers for the same vessel share one common time window. Time windows can be optimized with different strategies. In this paper, we first propose a framework for installing this solution in a terminal system, and second develop an optimization model for scaling time windows with three alternative strategies: namely fixed ending-point strategy (FEP), variable end-point strategy and greedy algorithm strategy. Third, to compare the strategies in terms of effectiveness, numerical experiments are conducted based on real data. The result shows that (1) good planning coordination is essential for the proposed method; and (2) FEP is found to be a better strategy than the other two.