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Stowage planning is at the essence of a maritime supply chain, especially for short sea Ro-Ro ships. This paper studies stowage optimisation of Ro-Ro ships with a focus on stability constraints and the applicability of models. The paper contributes to short sea Ro-Ro ship stowage in two ways. First, we propose an integrated approach of designing stowage models with the consideration of loading computers. Second, we present a mathematical formulation of the Ro-Ro Ship Stowage Problem with Ballast Water with a discretisation method, to generate an optimal stowage plan which meets stability requirements by means of the weight of cargoes instead of excess ballast water, i.e. excess fuel consumption. Computational tests based on empirical data indicate significant savings and potential of model application in the real world. Preliminary results show 57.69% ballast water reduction, equivalent to 6.7% fuel savings and CO2 reduction. Additional tests on instances with various cargo weight distribution and discretisation levels are conducted, and finally, improvements are suggested for further research considerations.

Ultra Large Container Vessels (ULCV) with high trade volumes per port are making fewer calls per round trip with more transshipment cargo and more port times at higher costs. Innovations which increase handling productivity and streamline handling operations of feeder vessels (in short: feeders) are required to avoid inefficient long stays in ports as well as to reduce the costs resulting from ULCV processing. The patented “Integrated Terminal Ship System” (ITSS or ITS system) satisfies these requirements by the innovation of direct container handling between ULCV and feeder vessels. Basically, there are two technical solutions possible: Transshipment containers are simultaneously handled on both ULCV sides using two finger piers (first alternative) and im-/export containers are un-/loaded at the ULCV quayside while transshipment containers are directly handled between ULCV and feeders at the ULCV waterside using one finger pier (second alternative). Both ITSS system alternatives use traction engines which move on the finger pier(s). The engines facilitate direct handling of transshipment containers by shifting the feeder vessel(s) alongside the pier(s) to the respective container bays required as per stowage plans.

The problem of determining the stowage plan for each port included in the circular route of a containership is the planning problem daily faced by the shipping line coordinator (SC). The SC has to determine how to stow in each port a given set of containers into bay locations, either on the deck or in the hold, in order to satisfy some structural and operational constraints, all the transport demand and minimize the ship berthing time. The time spent by the ship at the ports is minimized when the SC defines a stowage plan that avoids re-handles, balances the workload of the cranes working in parallel at each port and requires to open and close hatches the least possible when moving containers in the holds. We propose a new MIP formulation, and a MIP heuristics for finding stowage plans for containerships up to 18000 TEUs. Moreover, the new formulation includes new and more accurate stability conditions.

Container liner shipping is about matching spare capacity to cargo in need of transport. This can be realized using cargo flow networks, where edges are associated with vessel capacity. It is hard, though, to calculate free capacity of container vessels unless full-blown non-linear stowage optimization models are applied. This may cause such flow network optimization to be intractable. To address this challenge, we introduce the Standard Capacity Model (SCM). SCMs are succinct linear capacity models derived from vessel data that can be integrated in higher order optimization models as mentioned above. In this paper, we introduce the hydrostatic core of the SCM. Our results show that it can predict key parameters like draft, trim, and stress forces accurately and thus can model capacity reductions due to these factors.

This paper deals with the container stowage planning problem, an important and a complex problem in maritime logistic optimization. The variant tackled in this work involves several constraints, inspired by real-life problems and application found in the literature. Given the complexity of the problem, which belongs to the class of \(\mathcal {NP}\)-hard problems, a novel evolutionary metaheuristic algorithm is developed and designed. Considering the ability and flexibility of Genetic Algorithm (GA). The approach is based on a two-phase procedure, one for master planning and the other for allocation of the containers into slots. GA parameters are analyzed to achieve practical and best results. The system offers stowage allocation solutions for both phases, thus offering flexibility for a wide variety of vessels and route combinations.

The United States has announced plans to continue supporting the International Space Station (ISS) through at least the year 2024. NASA, working with the other ISS International Partners, will continue to foster greater use of the ISS platform, for both research and commercial activities, while using the ISS as a base for expanding the commercial use of low Earth orbit (LEO). In the United States, NASA remains the primary supplier of capabilities and services in LEO, such as habitation systems, power, cooling, crew health equipment, upmass and sample return, research facilities, cold stowage, crew time, and data transmission. Access to LEO for ISS cargo has already been transitioned from a primary government activity to a commercially supplied capability through the development and operations of the Commercial Cargo Services providers. NASA is in the process of developing commercial crew transportation system capabilities also in support to the ISS. It is the goal of NASA to evolve these systems and capabilities through the ISS Program in such a way that they will support market driven commercial research, as well as NASA’s long-term exploration plans. NASA will continue to make investments in these areas through at least 2024 to ensure continue access to LEO. This paper will examine the intersection of the growing commercial transportation and research markets, as well as the ways in which the transition from government to commercial activity in LEO might unfold.

Container stowage planning is a complex task in which multiple objectives have to be optimized while ensuring that the stowage rules as well as the safety and balance requirements are observed. Most algorithms for solving the problem are comprised of 2 parts: a container-location selection mechanism and a constraint evaluation engine. The former selects one or more container-location pairs for allocation iteratively and the latter evaluates whether the selected container-location pairs violate any of the constraints. We observe that, using the same selection mechanism, the order in which the constraints are evaluated can have significant impact on the overall efficiency. We propose Sequential Sample Model (SSM) as an improvement over the existing Random Sample Model (RSM) for analysis of the problem. We present and evaluate several strategies in optimizing the constraint evaluation engine. We show how to achieve the optimal constraint ordering with respect to SSM. However, such ordering requires perfect information on the constraint tests which is impractical. We present alternative strategies and show empirically that their efficiencies are close to the optimum. Experiments show that, when compared to an arbitrary ordering, an average of 2.42 times speed up in the evaluation engine can be achieved.

Stowage planning has an important effect in container shipping and is also a hard combinatorial problem. In order to improve the operation efficiency and reduce the cost, a new optimization method called Social Network-based Swarm Optimization Algorithm (SNSO) is applied to solve the slot planning problem of container vessel bays. As a swarm intelligence optimization algorithm, SNSO is designed with considering population topology, neighborhood and individual behavior comprehensively to improve the swarm search ability. An effective coding and decoding strategy is proposed to optimize the slot planning problem for using SNSO. Finally, fourteen cases of slot planning with different scales are selected to test the proposed algorithm and five swarm intelligence algorithms are selected for comparison in the experiment. The results show that the SNSO has a better performance on solving stowage plan problem in the terms of convergence and accuracy than other selected algorithms.

Roll-on/Roll-off (RoRo) ships represent the primary source for transporting vehicles and other types of rolling material over long distances. In this paper we focus on operational decisions related to stowage of cargoes for a RoRo ship voyage visiting a given set of loading and unloading ports. By focusing on stowage on one deck on board the ship, this can be viewed as a special version of a 2-dimensional packing problem with a number of additional considerations, such as one wants to place vehicles that belong to the same shipment close to each other to ease the loading and unloading. Another important aspect of this problem is shifting, which means temporarily moving some vehicles to make an entry/exit route for the vehicles that are to be loaded/unloaded at the given port. We present several versions of a new mixed integer programming (MIP) formulation for the problem. Computational results show that the model provides good solutions on small sized problem instances.

In parallel with preparing Dragons for their commercial cargo missions, SpaceX was also busy developing its manned variant – the V2. In 2013,NASA had outlined its strategy for enabling the certification of commercial crew systems with a goal of restoring domestic manned launch capability by 2017. Phase 1 of this certification strategy was the Certification Products Contract (CRC), which was awarded to SpaceX, along with Boeing and Sierra Nevada in 2012. The contract was based on Federal Acquisition Regulations (FAR) that required the companies to adhere to specific standards such as hazards analysis, verification, and validation. The next phase of the contract went into action in the summer of 2014 with the awarding of funding to SpaceX and Boeing and the de-selection of Sierra Nevada. This phase of the contract outlined the Design, Development, Test, and Evaluation (DDTE) milestones that were required for NASA to certify a man-rated vehicle. Under the CCtCap certification, SpaceX was required to check off certain DDTE tasks, one of which included one manned test flight to the International Space Station (ISS) between July 2014 and September 2017. Once the first man-rated mission was checked off, NASA required at least two post-certification missions (PCMs) to be flown, although the maximum number of PCMs for each Boeing and SpaceX was six. These PCMs could be flown until 31 December 2020. Another important item encompassed special studies that Boeing and SpaceX would be required to perform to reduce risk in their man-rated vehicle, and a fourth item was complying with the cargo requirement of the manned vehicle, which was defined as a minimum of 100 kilograms of stowage volume, or an additional 100 kilograms for a seat that is not taken by a crewmember.

Recent statistics show that large container terminals can process more than 30 million containers a year, and are constantly in search for the better ways to optimize processing time, deliver high quality and profitable services. Some of the terminal decisions are, however, dependent on externalities. One of those is the ship loading process. Based on the stowage plan received by liner shippers, terminal operators plan the execution of load and discharge operations. In this paper we present a literature review for the Ship Loading Problem, where stowage and loading sequencing and scheduling are integrated to improve the efficiency of the ship handling operations. We present a survey of the state-of-the-art methods and of the available benchmarking data.

Containers stowage optimization is a long-standing problem in the maritime industry. Since the problem was shown to be NP-hard, it is computationally challenging to obtain an optimal solution. We first review an efficient 2-phase block stowage scheme which can generate a feasible initial solution in just a few minutes. Since the algorithm relies heavily on checking whether any constraints will be violated by stowing a container at a specific location, we investigate the impact of changing the order in which the constraints are checked on the execution time of the algorithm. We evaluate seven different strategies for ordering the sequence in which the constraints are tested. Experiments based on real stowage instances show that, by strategically reordering the constraints test sequence, we can achieve 2 times speedup on the stowage planning algorithm on average, and up to 33 times speedup in certain instances.

This chapter addresses the problem of determining stowage plans for containers into a ship, which is the so-called master bay plan problem (MBPP). As a novel issue and variant of MBPP, in the present work we consider the stowage of hazardous containers that follows the principles included in the segregation table of the International Maritime Dangerous Goods (IMDG) Code. Formally, the MBPP consists in determining how to stow a set of n containers, split into different groups, according to their size, type, class of weight and destinations, into a set of m available slots, that are locations either on the deck or in the stow, of predetermined bays of a containership. Some structural and operational constraints, related to both the containers and the ship, have to be satisfied. The need of stowing dangerous goods implies to take into account additional constraints to be verified in each slot concerning the safety of the whole cargo, for which dangerous goods are categorized into different types and forced to be stowed away from incompatible ones. We face such variant of MBPP on the basis of its relationship with the bin packing problem, where items are containers and the bins are sections of the ship available for the stowage of hazardous containers. In particular, following a step by step procedure for properly loading all containers on board, we show how the segregation rules derived from the IMDG Code impact on the available slots of the bins. A real life case study is reported.

The master bay planning problem (MBPP) arises in the context of maritime transportation. In particular, MBPP consists of determining an efficient plan to stowage the containers into the containership such that the total loading time is minimized. This problem is classified as NP-hard due to the large number of possible solutions generated by the combination of assigning containers to locations in the containership. These solutions are both feasible and infeasible, which increases even more the hardness of MBPP. To deal with this problem, there are several exact and heuristic approaches that are well documented in the literature. One of the most important exact methods is in the form of an integer linear programming (ILP) formulation. However, the number of variables and constraints generated by this ILP model is very large. In this chapter, we propose a new exact algorithm based on a branch and bound (B&B) approach. The main feature is the usage of an efficient representation structure of candidate solutions. We test the proposed B&B on a set of small-sized instances. Experimental results demonstrate that, within this set of instances, our B&B is competitive with respect to the ILP model from the literature.

Short sea shipping has several advantages over other means of transportation, recognized by EU members. The maritime transportation could be dealt like a combination of two well-known problems: the container stowage problem and routing planning problem. The integration of these two well-known problems results in a new problem CSSRP (Container stowage and ship routing problem) that is also an hard combinatorial optimization problem. The aim of this work is to solve the CSSRP using a mixed integer programming model. It is proved that regardless the complexity of this problem, optimal solutions could be achieved in a reduced computational time. For testing the mathematical model some problems based on real data were generated and a sensibility analysis was performed.

In May 2015, NASA’s BEAM (Bigelow Expandable Activity Module) mission will feature the robotic berthing of one of Bigelow Aerospace’s BEAM modules at one of the interfaces of the International Space Station’s (ISS) Tranquility node. As well as providing much-needed logistics and stowage support for the orbiting outpost, BEAM is expected to provide useful data on the performance of non-rigid space station modules. As discussed in the previous chapter, expandable space station modules are nothing new. But the TransHab discussed in Chapter 2 is not quite the same as the BEAM. While TransHab proved many of the aspects of inflatable module technology, there was still much work to be done. For example, although the NASA team had begun working on how to incorporate windows, putting holes in the module walls complicated the engineering of the skin so much that they left windows out of the proposed ISS module design entirely, although NASA filed a patent describing how windows could be fitted into a module. While details of how Bigelow has evolved the TransHab technology have not been published, one of the improvements was to include windows. To that end, Bigelow engineers conducted pressurization tests of different module designs, often deliberately pushing designs beyond their limits to establish safety margins. These tests often resulted in the module exploding. Fortunately, the team followed NASA’s experience by conducting the pressurization tests under water, which muted the explosive force when the modules ripped apart. In one test, engineers decided to conduct a fill-to-fail test in open air and discovered why NASA hadn’t when the test module exploded with such force that it shook the foundation of the building. In short, TransHab was a starting point for Bigelow’s BEAMs, which is why it’s worthwhile taking a look at the TransHab technology a little more closely.

The general subject of packing objects, exploiting the available volume, as much as possible, has represented, for centuries, or even longer, an extremely tricky task. This issue seems trivial, until one encounters it. The question arose, for instance, when dealing with cannon ball stowage in ancient vessels. It is not surprising at all that it gained the role of the packing issue par excellence, when Hilbert announced his eighteenth problem (to date resolved by computer-assisted proof, e.g. Gray 2000). It concerned the accommodation of equal spheres, attaining the maximum density. This chapter introduces the overall subject of packing.

The stowage stack minimization problem with zero rehandle constraint SSMP-ZR aims to find a minimum number of stacks to accommodate all the containers in a multi-port voyage without occurring container rehandles. In this paper, we first give the integer models of the SSMP-ZR with uncapacitated and capacitated stack height. Next, heuristic algorithms are proposed to construct solutions to the SSMP-ZR. The theoretical performance guarantee of the algorithms is then discussed. To evaluate the actual performance of the algorithms, we conduct experiments on a set of instances with practical size. The results demonstrate that our heuristic approaches can generate very promising solutions compared with the random loading solutions and integer programming solutions by CPLEX.