Integrated Models and Tools for Design and Management of Global Supply Chain

In modern and global supply chain, the increasing trend toward product variety, level of service, short delivery delay and response time to consumers, highlight the importance to set and configure smooth and efficient logistic processes and operations. In order to comply such purposes the supply chain management (SCM) theory entails a wide set of models, algorithms, procedure, tools and best practices for the design, the management and control of articulated supply chain networks and logistics nodes. The purpose of this Ph.D. dissertation is going in detail on the principle aspects and concerns of supply chain network and warehousing systems, by proposing and illustrating useful methods, procedures and support-decision tools for the design and management of real instance applications, such those currently face by enterprises. In particular, after a comprehensive literature review of the principal warehousing issues and entities, the manuscript focuses on design top-down procedure for both less-than-unit-load OPS and unit-load storage systems. For both, decision-support software platforms are illustrated as useful tools to address the optimization of the warehousing performances and efficiency metrics. The development of such interfaces enables to test the effectiveness of the proposed hierarchical top-down procedure with huge real case studies, taken by industry applications. Whether the large part of the manuscript deals with micro concerns of warehousing nodes, also macro issues and aspects related to the planning, design, and management of the whole supply chain are enquired and discussed. The integration of macro criticalities, such as the design of the supply chain infrastructure and the placement of the logistic nodes, with micro concerns, such the design of warehousing nodes and the management of material handling, is addressed through the definition of integrated models and procedures, involving the overall supply chain and the whole product life cycle. A new integrated perspective should be applied in study and planning of global supply chains. Each aspect of the reality influences the others. Each product consumed by a customer tells a story, made by activities, transformations, handling, processes, traveling around the world. Each step of this story accounts costs, time, resources exploitation, labor, waste, pollution. The economical and environmental sustainability of the modern global supply chain is the challenge to face

Performance and Safety Enhancement Strategies in Vehicle Dynamics and Ground Contact

Recent trends in vehicle engineering are testament to the great efforts that scientists and industries have made to seek solutions to enhance both the performance and safety of vehicular systems. This Special Issue aims to contribute to the study of modern vehicle dynamics, attracting recent experimental and in-simulation advances that are the basis for current technological growth and future mobility. The area involves research, studies, and projects derived from vehicle dynamics that aim to enhance vehicle performance in terms of handling, comfort, and adherence, and to examine safety optimization in the emerging contexts of smart, connected, and autonomous driving.This Special Issue focuses on new findings in the following topics:(1) Experimental and modelling activities that aim to investigate interaction phenomena from the macroscale, analyzing vehicle data, to the microscale, accounting for local contact mechanics; (2) Control strategies focused on vehicle performance enhancement, in terms of handling/grip, comfort and safety for passengers, motorsports, and future mobility scenarios; (3) Innovative technologies to improve the safety and performance of the vehicle and its subsystems; (4) Identification of vehicle and tire/wheel model parameters and status with innovative methodologies and algorithms; (5) Implementation of real-time software, logics, and models in onboard architectures and driving simulators; (6) Studies and analyses oriented toward the correlation among the factors affecting vehicle performance and safety; (7) Application use cases in road and off-road vehicles, e-bikes, motorcycles, buses, trucks, etc

Efficient Material Flow in Mixed Model Assembly Lines

Effizienten Materialfluss im Mischmodell Montagelinien Diese Arbeit untersucht ein Materialflusssystem für Fließlinien zur Fertigung von variantenreichen Produkten. Sogenannte Routenzüge kommen häufig zur Bereitstellung von Teilen an den Arbeitsstationen einer Variantenfließlinien zum Einsatz. Die Teile werden in kleinen Behältern (Kleinladungsträgern) im Zentrallager oder in verteilten Zwischenlagern, sogenannten Supermärkten, auf den Routenzug geladen. Bei jeder Tour des Routenzugs werden mehrere Arbeitsstationen mit Kleinladungsträgern versorgt. Der zeitliche Abstand zwischen zwei Belieferungen definiert die Zugumlaufzeit. Ein derartiges Materialbereitstellungssystem, bezeichnet als In-Plant Milk Run, reduziert Bestands- und Transportkosten, weil es eine regelmäßige Just-in-Time Anlieferung der Materialien realisiert. Außerdem bringt es aufgrund der Nutzung kleiner Behälter ergonomische Vorteile mit sich. Weiterhin sinkt das Unfallrisiko. Deshalb findet das In-plant Milk Run System als Alternative zu Gabelstaplern in vielen Branchen, insbesondere in der Automobilindustrie, zunehmend Verwendung. Die Untersuchung dieser Systeme und die optimale Auswahl ihrer Parameter sind wichtige Anliegen, um die Transport- und Bestandskosten zu reduzieren. Diese Arbeit unterscheidet bei der Gestaltung von In-plant Milk Run Systemen fünf verschiedene Problemstellungen (Systeme). Für jedes System wird ein bestimmtes Planungsvorgehen zur Minimierung der kritischen Kosten vorgeschlagen. Zwischen den Systemen gibt es sowohl Ähnlichkeiten als auch Unterschiede. Die Methodik verwendet genetische Algorithmen, ganzzahlige und dynamische Programmierung, Simulation und analytische Untersuchung. Die fünf Systeme werden anhand von Kriterien klassifiziert. Als solche werden das Ausmaß von Störungen, die Verfügbarkeit der Routenzüge, die Genauigkeit der Materialbedarfsschätzungen, die Länge der Montagelinien, der mittlerer Teilebedarf und die Verfügbarkeit von technischer Infrastruktur, wie RFID- oder Barcode-Systemen, herangezogen. Unterschieden werden damit das bedarfsorientierte Zentrallager, der dezentrale bedarfsorientierte Supermarkt, das traditionelle Kanban-System, das elektronische Kanban-System und ein hybrides System, bestehend aus bedarfsorientiertem und e-Kanban-System. Dabei kann das Kanban-System sowohl im Zentrallager, als auch im System dezentraler Supermärkte zum Einsatz kommen. In bedarfsorientierten Systemen wird der Materialbedarf der Arbeitsstationen für eine gewisse Anzahl an Schichten aus der Produktionssequenz und den entsprechenden Stücklisten abgeleitet und ist damit exakt bekannt. In allen Systemen werden einige Restriktionen berücksichtigt. Hierunter fallen die Routenzugkapazität, die Dauer einer Tour und die Kapazität der Lagerflächen direkt an der Montagelinie. In jedem System sind drei Entscheidungsprobleme, das Routing, Scheduling und Loading Problem, zu lösen. Das Routing Problem beinhaltet die Zuordnung von Zügen zu Gruppen von Arbeitsstationen. Im Scheduling Problem werden die Zugumlaufzeit und der Zeitpunkt der ersten Belieferung für jeden Routenzug festgelegt. Die Lösung des Loading Problems erfordert die Determinierung von Art und Menge der in jedem Zyklus und an jede Arbeitsstation ausgelieferten Behälter. Im Falle des Vorhandenseins von Zyklen, in denen der Materialbedarf an einzelnen Arbeitsstationen die Routenzugkapazität übersteigt, werden einige Behälter vorzeitig angeliefert. Dieser Fall wird als „Early Loading“ bezeichnet und tritt in Kanban-Systemen nicht auf. Im System dezentraler bedarfsorientierter Supermärkte ist zusätzlich die Anzahl und der Standort der Supermärkte zu bestimmen („Supermarket Location Problem“). Im traditionellen Kanban-System erfolgt die Festlegung der Kanbanzahl basierend auf dem Zielkonflikt zwischen mittlerem Linienbestand und Fehlbestandswahrscheinlichkeit. Im e-Kanban-System wird der Umfang des zirkulierenden Bestands analog bestimmt. Außerdem wird ein neues Konzept, der sogenannte „Adjusted Electronic Kanban“, zur Behandlung von Kapazitätsengpässen des Routenzugs vorgestellt. Die Ergebnisse sind abhängig vom betrachteten System. Die Leistungsfähigkeit des genetischen Algorithmus zur Lösung des Supermarket Location Problems wurde anhand der Ergebnisqualität, CPU Zeit und der Variabilität dieser beiden Größen untersucht. Es wurden akzeptable CPU Zeiten und eine hohe Ergebnisqualität erreicht. Die Leistungsfähigkeit der drei Kanban-Systeme wurde unter Verwendung von Simulation getestet. Hierbei wurde die Vorteilhaftigkeit des Adjusted Electronic Kanban insbesondere im Fall begrenzter Routenzugkapazität bewiesen. Der inverse Zusammenhang zwischen mittlerem Linienbestand und Fehlbestandswahrscheinlichkeit konnte aufgezeigt werden. Im Falle der bedarfsorientierten Systeme wurde der Effekt von dynamischer Disposition, Early Loading und Minimierung der Anzahl zusätzlicher Anhänger deutlich gemacht. Bei Verwendung des hybriden Systems aus e-Kanban und bedarfsorientiertem System, liefert die dynamische Disposition in Bezug auf die Verarbeitung von Störungenerheblich bessere Resultate als die Einzelsysteme, insbesondere bei hohem Materialbedarf an den Arbeitsstationen.This study investigates the material handling system used in mixed model assembly lines which are important to produce diversified product models to satisfy the increasing customer demand. Tugger trains are used to feed by parts the workstations in the assembly lines. These parts are loaded on trains in small containers (bins) from the warehouse or intermediate stores scattered in the factory. These stores are called supermarkets, which are closer to workstations than the main warehouse. In each train tour, several workstations are replenished by bins every a certain time period called train cycle time. This replenishment system is called in-plant milk run which is used to reduce inventory and transportation costs because of its dependence on repetitive just-in-time parts delivery. Besides reducing costs, ergonomic advantages are obtained due to the use of small-sized bins. Safety hazards are also reduced. As an alternative to forklift system, in-plant milk run was used by several industries especially the automotive industry. It is important to investigate this system and to design its parameters to reduce the total material handling and inventory cost. The study divides the general problem to five different problems (systems) based on the situation on the ground. For each system, a certain planning approach is designed to optimize the parameters of the system to minimize its critical costs. There are some similarities and differences between the systems. The methodology is based on genetic algorithm, integer programming, dynamic programming, simulation, and analytical investigation. The five different systems are classified based on factors such as level of assembly line disturbances, availability of tugger trains, accuracy of expectation of workstations demand for parts, the length of assembly lines and their average demand for parts, and the availability of technical infrastructure such as radio frequency identification (RFID) or bar code technologies. These systems are main warehouse demand-oriented, decentralized supermarket demand-oriented, traditional kanban, electronic kanban, and a hybrid system of e-kanban and demand-oriented systems. The two kanban systems can be applied in both main warehouse and decentralized supermarkets systems. In demand-oriented systems, the exact workstations demand for parts is assumed to be known for the next few shifts based on the predetermined sequencing of product models and needed parts for each product model. Generally some constraints are considered in all the five systems. These constraints are tugger train capacity, tour time, and the capacity of area beside stations. There are three general problems that must be investigated in the systems. These problems are routing, scheduling, and loading problems. Routing problem is the assignment of trains to different stations. In scheduling problem, the train cycle time and the beginning of the movement of the each train are determined. In loading problem, the type and quantities of bins delivered in each train cycle to each workstation are determined. In the case that there are some peak demand periods in which the total stations demand for parts is more than the tugger trains capacity, some bins are delivered before they are needed. This case is called ‘early loading’. Early loading does not exist in both the traditional and electronic kanban systems. In decentralized supermarket demand-oriented system, the location and number of supermarkets are determined. In traditional kanban system, the number of kanban is determined based on the tradeoff between the average line-side inventory and workstation starvation. In e-kanban, the size of circulating inventory in the system is determined for the same purpose. A new approach namely, adjusted electronic kanban, is presented to accommodate train capacity problems. The results depend on the systems investigated. The performance of genetic algorithm used in supermarket location problem was tested based on the quality of the results, CPU time, and variability in both of them. Reasonable CPU time and high quality of results were obtained. The performances of e-kanban, adjusted electronic kanban, and traditional kanban were tested using simulation, where the superiority of adjusted electronic kanban was proven especially in the case of limited tugger trains capacity. The inverse relationship between the average line-side inventory and workstation starvation was presented. In the case of demand-oriented system, the effects of using dynamic scheduling, early loading, and the objective of minimizing the number of extra trailers were obvious to reduce the problems of tugger train limited capacity. In the case of using the hybrid system of e-kanban and demand-oriented systems, the dynamic planning approach outperforms the traditional systems to accommodate the line disturbances especially in the case of large workstations demand

New Methods for ferrous raw materials characterization in electric steelmaking

425 p.In the siderurgical sector, the steel scrap is the most important raw material in electric steelmaking,contributing between 70% of the total production costs. It is well-known how the degree of which thescrap mix can be optimized, and also the degree of which the melting operation can be controlled andautomated, is limited by the knowledge of the properties of the scrap and other raw-materials in thecharge mix.Therefore, it is of strategic importance having accurate information about the scrap composition of thedifferent steel scrap types. In other words, knowing scrap characteristics is a key point in order to managethe steel-shop resources, optimize the scrap charge mix/composition at the electric arc furnace (EAF),increase the plant productivity, minimize the environmental footprint of steelmaking activities and tohave the lowest total cost of ownership of the plant.As a main objective of present doctoral thesis, the doctorate will provide new tools and methods of scrapcharacterization to increase the current recycling ration, through better knowledge of the quality of thescrap, and thus go in the direction of a 100% recycling ratio. In order to achieve it, two main workinglines were developed in present research. Firstly, it was analysed not only the different existingmethodologies for scrap characterization and EAF process optimization, but also to develop new methodsor combination of existing, Secondly, it was defined a general recommendations guide for implementingthese methods based on the specifics of each plant

Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research

Advanced Industrial Robot Control Systems

The objective of this research is to extend the flexibility a,hd . \u27usefulness. of current industrial robots by the integration of robot motion control directly into a. general purpose programming language, the development of force feedback and its integration into the language, the formulation of a high-level task description language RTM, and by the investigation of both off-line collision-free path planning and on-line collision avoidance

XVIII International Coal Preparation Congress

Changes in economic and market conditions of mineral raw materials in recent years have greatly increased demands on the ef fi ciency of mining production. This is certainly true of the coal industry. World coal consumption is growing faster than other types of fuel and in the past year it exceeded 7.6 billion tons. Coal extraction and processing technology are continuously evolving, becoming more economical and environmentally friendly. “ Clean coal ” technology is becoming increasingly popular. Coal chemistry, production of new materials and pharmacology are now added to the traditional use areas — power industry and metallurgy. The leading role in the development of new areas of coal use belongs to preparation technology and advanced coal processing. Hi-tech modern technology and the increasing interna- tional demand for its effectiveness and ef fi ciency put completely new goals for the University. Our main task is to develop a new generation of workforce capacity and research in line with global trends in the development of science and technology to address critical industry issues. Today Russia, like the rest of the world faces rapid and profound changes affecting all spheres of life. The de fi ning feature of modern era has been a rapid development of high technology, intellectual capital being its main asset and resource. The dynamics of scienti fi c and technological development requires acti- vation of University research activities. The University must be a generator of ideas to meet the needs of the economy and national development. Due to the high intellectual potential, University expert mission becomes more and more called for and is capable of providing professional assessment and building science-based predictions in various fi elds. Coal industry, as well as the whole fuel and energy sector of the global economy is growing fast. Global multinational energy companies are less likely to be under state in fl uence and will soon become the main mechanism for the rapid spread of technologies based on new knowledge. Mineral resources will have an even greater impact on the stability of the economies of many countries. Current progress in the technology of coal-based gas synthesis is not just a change in the traditional energy markets, but the emergence of new products of direct consumption, obtained from coal, such as synthetic fuels, chemicals and agrochemical products. All this requires a revision of the value of coal in the modern world economy

The selection and evaluation of a sensory technology for interaction in a warehouse environment

In recent years, Human-Computer Interaction (HCI) has become a significant part of modern life as it has improved human performance in the completion of daily tasks in using computerised systems. The increase in the variety of bio-sensing and wearable technologies on the market has propelled designers towards designing more efficient, effective and fully natural User-Interfaces (UI), such as the Brain-Computer Interface (BCI) and the Muscle-Computer Interface (MCI). BCI and MCI have been used for various purposes, such as controlling wheelchairs, piloting drones, providing alphanumeric inputs into a system and improving sports performance. Various challenges are experienced by workers in a warehouse environment. Because they often have to carry objects (referred to as hands-full) it is difficult to interact with traditional devices. Noise undeniably exists in some industrial environments and it is known as a major factor that causes communication problems. This has reduced the popularity of using verbal interfaces with computer applications, such as Warehouse Management Systems. Another factor that effects the performance of workers are action slips caused by a lack of concentration during, for example, routine picking activities. This can have a negative impact on job performance and allow a worker to incorrectly execute a task in a warehouse environment. This research project investigated the current challenges workers experience in a warehouse environment and the technologies utilised in this environment. The latest automation and identification systems and technologies are identified and discussed, specifically the technologies which have addressed known problems. Sensory technologies were identified that enable interaction between a human and a computerised warehouse environment. Biological and natural behaviours of humans which are applicable in the interaction with a computerised environment were described and discussed. The interactive behaviours included the visionary, auditory, speech production and physiological movement where other natural human behaviours such paying attention, action slips and the action of counting items were investigated. A number of modern sensory technologies, devices and techniques for HCI were identified with the aim of selecting and evaluating an appropriate sensory technology for MCI. iii MCI technologies enable a computer system to recognise hand and other gestures of a user, creating means of direct interaction between a user and a computer as they are able to detect specific features extracted from a specific biological or physiological activity. Thereafter, Machine Learning (ML) is applied in order to train a computer system to detect these features and convert them to a computer interface. An application of biomedical signals (bio-signals) in HCI using a MYO Armband for MCI is presented. An MCI prototype (MCIp) was developed and implemented to allow a user to provide input to an HCI, in a hands-free and hands-full situation. The MCIp was designed and developed to recognise the hand-finger gestures of a person when both hands are free or when holding an object, such a cardboard box. The MCIp applies an Artificial Neural Network (ANN) to classify features extracted from the surface Electromyography signals acquired by the MYO Armband around the forearm muscle. The MCIp provided the results of data classification for gesture recognition to an accuracy level of 34.87% with a hands-free situation. This was done by employing the ANN. The MCIp, furthermore, enabled users to provide numeric inputs to the MCIp system hands-full with an accuracy of 59.7% after a training session for each gesture of only 10 seconds. The results were obtained using eight participants. Similar experimentation with the MYO Armband has not been found to be reported in any literature at submission of this document. Based on this novel experimentation, the main contribution of this research study is a suggestion that the application of a MYO Armband, as a commercially available muscle-sensing device on the market, has the potential as an MCI to recognise the finger gestures hands-free and hands-full. An accurate MCI can increase the efficiency and effectiveness of an HCI tool when it is applied to different applications in a warehouse where noise and hands-full activities pose a challenge. Future work to improve its accuracy is proposed

New solution approaches for optimization problems with combinatorial aspects in logistics management

This dissertation comprises five papers, which have been published in scientific journals between 2019 and 2022. The papers consider logistic optimization problems from three different subjects with a focus on intra-logistics. All considered optimization problems have strong combinatorial aspects. To solve the considered problems, various solution approaches including different decomposition techniques are employed. Paper 1 investigates the optimization of the layout and storage assignment in warehouses with U-shaped order picking zones. The paper considers two objectives, namely minimizing the order picker's walking distance and physical strain during order picking. To solve the problem, a semantic decomposition approach is proposed, which solves the problem in polynomial time. In a computational study, both considered objectives are found to be mostly complementary. Moreover, suggestions for advantageous layout designs and storage assignments are derived. Paper 2 considers the problem of how to stow bins on tow trains in order to minimize the handling personnel's physical strain for loading and unloading. The problem is shown to be NP-hard and decomposed semantically. Utilising the decomposition, the problem is solved exactly with dynamic programming and heuristically with a greedy randomized adaptive search procedure. A consecutive computational study shows that both procedures perform well. Beyond that, it investigates the influence of the tow train wagons' design on the considered objective. Paper 3 is concerned with the problem of scheduling jobs with time windows on unrelated parallel machines, which is a NP-hard optimization problem that has applications, i.a., in berth allocation and truck dock scheduling. The paper presents an exact logic-based Benders decomposition procedure and a heuristic solution approach based on a set partitioning formulation of the problem. Moreover, three distinct objectives, namely minimizing the makespan, the maximum flow time, and the maximum lateness are considered. Both procedures exhibit good performances in the concluding computational study. Paper 4 addresses the problem of order picker routing in a U-shaped order picking zone with the objective of minimizing the covered walking distance. The problem is proven to be NP-hard. An exact logic-based Benders decomposition procedure as well as a heuristic dynamic programming approach are developed and shown to perform well in computational tests. Beyond that, the paper discusses different storage assignment policies and compares them in a numeric study. Paper 5 studies scheduling electrically powered tow trains in in-plant production logistics. The problem is regarded as an Electric Vehicle Scheduling Problem, where tow trains must be assigned to timetabled service trips. Since the tow trains' range is limited, charging breaks need to be scheduled in-between trips, which require detours and time. The objective consists in minimizing the required fleet size. The problem is shown to be NP-hard. To solve the problem, Paper 5 proposes a branch-and-check approach that is applicable for various charging technologies, including battery swapping and plug-in charging with nonlinear charge increase. In a computational study, the approach's practical applicability is demonstrated. Moreover, influences of the batteries' maximum capacity and employed charging technology are investigated