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Classification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as cost-sensitivity, explainability, and fairness, and to deal with complex data, such as functional data.

Bacteria sense intracellular and environmental signals using an array of proteins as antennas. The information is transmit- ted from such sensory modules to other protein domains that act as output effectors. Sensor and effector can be part of the same polypeptide or instead be separate diffusible proteins that interact specifically. The output effector modules regulate physiologic responses, allowing the cells to adapt to the varying conditions. These biological machineries are known as signal transduction systems (STSs). Despite the captivating architectural diversity exhibited by STS proteins, a universal feature is their allosteric regulation: signal binding at one site modifies the activity at a physically distant site. Allostery requires protein plasticity, precisely encoded within their 3D structures, and implicating programmed molecular motions. This review summarizes how STS proteins connect stimuli to specific responses by exploiting allostery and protein plasticity. Illustrative examples spanning a wide variety of protein folds will focus on one- and two-component systems (TCSs). The former encompass the entire transmission route within a single polypeptide, whereas TCSs have evolved as separate dif- fusible proteins that interact specifically, sometimes including additional intermediary proteins in the pathway. Irrespective of their structural diversity, STS proteins are able to modulate their own molecular motions, which can be relatively slow, rigid-body movements, all the way to fast fluctuations in the form of macromolecular flexibility, thus spanning a continuous protein dynamics spectrum. In sum, STSs rely on allostery to steer information transmission, going from simple two-state switching to rich multi-state conformational order/disorder transitions. Agencia Nacional de Investigación e Innovación Unit of Integrative Microbiology of Zoonotic Agents IMiZA, Joint International Units program, Institut Pasteur/Institut Pasteur de Montevideo.

During this section, the challenges addressed with this contribution are described and formulated as research questions. As being the main issue, the challenges with modularization methods supplying various alternatives for MPAs and corresponding modular kits are stated. Accordingly, this contribution is to supply a configuration-based approach, enabling the simulation of MPA alternatives in order to assess their individual performances already during their early design stages without the need for their implementation. This approach further is to determine the amongst the individual circumstances’ most performant degree of configurability vs. customer-individual design. The presented research aims to enable an increased flexibility, dynamic, transparency and consistency throughout the MPA development process and therefore, increased market competitiveness.

The Automotive industry is currently looking for efficient hybrid transmission concepts for use with a single electric machine. The aim is to find a cost effective alternative to current parallel and power-split hybrid concepts.

The ongoing transition towards electro-mobility requires an increased reactivity and reconfigurability in automotive assembly. However, the traditional line assembly, which is characterized by rigid cycle times and linear product flow, has already been pushed to its flexibility limits. Drivers are the increase of product changes, variants and derivatives within assembly lines. To further increase reactivity and reconfigurability, matrix structured assembly configurations are a possible solution. Several studies highlight the theoretical advantages, but it has not been applied and validated in industrial use-cases, due to the high transformational gap between line and matrix configurations. In contrast, segment-wise line-less structures show a high potential for this.

(a) Situation faced: One large set of problems in Australian agricultural supply chains comprises liquidity and payment certainty: Farmers are typically paid for their goods after long delays, and sometimes payment fails altogether. At the same time, first buyers of crops (exporters, traders, processors, feedlots) have limited funds as they, like the farmers, have little access to financing, such as obtaining a loan against a quantity of the harvested crops. Financiers are often unwilling or unable to offer financing against such assets because of insufficient visibility—that is, the ability to see and verify the assets’ information, or the high cost of obtaining it. These liquidity and financing shortages are harmful to the Australian agricultural industry. (b) Action taken: The AgriDigital founding team knew these issues first hand, and, seeing early on the opportunities created by blockchain technology for their industry, founded the company to address these key challenges using emerging technologies. Their core idea was that digital trust could be generated across agri-supply chains using blockchain technology to create secure digital assets representing harvested crops, which would then become the foundation data layer to facilitate trade and finance solutions. Effectively conducting process innovation across the whole industry, AgriDigital first ran three pilots to gain an understanding of the needs and requirements of the various stakeholders. (c) Results achieved: Based on these pilots, two outcomes were implemented: (1) the blockchain part of the technology was spun out into a foundation, Geora, and generalized for other industries, and (2) AgriDigital developed a final product using Geora’s blockchain technology to expose a registry of assets using which financiers could deliver financing in a secure and streamlined way. In the second half of 2019, 75 growers and 6 buyers were already using the combined solution, from which 122,000 bales of cotton and 34,000 tons of grain, with a total value of US$18.5 million, were financed. (d) Lessons learned: The key lesson learned from running the pilots was that the emerging blockchain technology can help to improve trust, asset management, and payments in agri-supply chains but that using blockchain technology requires specific, detailed expertise. Therefore, a digital toolkit that makes the benefits of blockchain to a wide range of users is of real value. Confidentiality and flexibility requirements among the stakeholders must also be ensured. Next to these technological lessons learned are that good behavior should be incented; supply chain stakeholders have low technical capabilities, and on-chain payments are still a challenge.

Flexibility provides a way to address technological progress and changing user needs in a vehicle’s architecture. Yet, this has serious implications on the development and manufacturing process. It necessitates additional investments in long-lasting interfaces and changeable production design. Within this paper, we address the research question “which components of a vehicle should be designed flexible and which parts can remain static to streamline investments in flexibility?”. We develop a methodology to support this decision by combining technology intelligence methods with comprehensive user acceptance research. Our approach builds upon a certain notion of function, which connects technological progress with customer utility. To this end, we analyze patents to evaluate technology dynamics and a user survey to detect functional needs. The research project FlexCAR designs a flexible vehicle architecture and was used for evaluation. Thereby, the methodology showed significant and sound results in its application and revealed flexible components with little effort. To the best of our knowledge, it is the first methodology of its kind.

We provide a UTXO model of blockchain transactions that is able to represent both credit and debt on the same blockchain. Ordinarily, the UTXO model is solely used to represent credit and the representation of credit and debit together is achieved using the account model because of its support for balances. However, the UTXO model provides superior privacy, safety, and scalability when compared to the account model. In this work, we introduce a UTXO model that has the flexibility of balances with the usual benefits of the UTXO model. This model extends the conventional UTXO model, which represents credits as unmatched outputs, by representing debts as unmatched inputs. We apply our model to solving the problem of transparency in reverse mortgage markets, in which some transparency is necessary for a healthy market but complete transparency leads to adverse outcomes. Here the pseudonymous properties of the UTXO model protect the privacy of loan recipients while still allowing an aggregate view of the loan market. We present a prototype of our implementation in Tendermint and discuss the design and its benefits.

One of the biggest challenges with the growing amount of renewable energy generation is the fluctuation in energy supply. In the case of Germany, renewable and volatile energy resources (wind and solar) are expanded. Industrial demand-side management, therefore, plays an important role for the automotive industry in Germany with its high energy demand. For a sustainable production manufacturing processes need to be more energy efficient and adaptable to volatile supply. The main goal is to synchronize manufacturing processes and their energy consumption with energy supply. While there are holistic concepts and ideas for a service-oriented platform for energy flexibility, a defined workflow for implementing energy flexibility signals at the shop-floor level is still missing. Our work proposes a method to adapt manufacturing processes with consideration to energy flexibility. The presented method aggregates data from a manufacturing process. Therefore, sensor data interoperates with flexibility signals on the shop-floor level where an intelligent controller can set process parameters by communicating through an OPC UA Server with the PLC. As an important cross-sectoral technology in the automotive industry, flexible compressed air is used for the validation of the presented method.

Flexibility for all different models within one body shop production line could be achieved without changing jigs, grippers as well as joining tools. A comprehensive analysis of the process chain formed the basis for the necessary development steps. In addition, planning methods based on mathematical optimization algorithms were developed and applied to cope with the complexity of the task. All the individual aspects are obtained, and the newly developed components were combined into a complete plant concept and its economic efficiency has been proven by Volkswagen planning department.

Current solutions for steer by wire force feedback actuators (FFA) based on steering column-integrated actuators require a huge effort for integration as design spaces used by head up display, climate systems or knee airbags are infringed. Additionally the crash element in the steering column limits the available retraction length of the steering wheel. A solution highly integrated in the steering wheel based on Joyson Safety Systems (JSS) active front steering concept enables the introduction of Steer-by-Wire (SbW) for any vehicle platform and automation level along with a maximum interior flexibility: Ranging from the pure integration of SbW-Systems in existing vehicle platforms to retractable or foldable and stowable steering wheels.

Volatile market demands, stronger regionalization of markets, ever-shortening product and innovation cycles as well as an ongoing demand for individualized products increase the need for adaptable production systems. More than a century after the start of mass production, alternative production systems are required to go beyond the current state of the art concerning adaptability, flexibility and reconfigurability to market requirements and demands. Fluid Manufacturing Systems (FLMS) describe such new production system concepts. The basic idea is to dynamically adapt and change all logistics and production processes, based on the comprehensive application of cyber-physical production systems (CPPS), thus enabling ongoing change in setup, configuration and product scope. CPPS provide a high degree of changeability, thus allowing for fast adaptions of the system to the changing requirements. Therefore the processes are continuously assessed, benchmarked and reconfigured to match the functional capabilities of production and logistic resources to the actual requirements originating from products and external influencing factors. Within this paper, conventional production systems such as Dedicated Manufacturing Lines (DML), Matrix Manufacturing System (MMS) and Flexible Manufacturing Systems (FMS) are described and characterized using defined criteria. The paper closes with a description of the Fluid Manufacturing System (FLMS), the core hypotheses and the advantages of the presented concept compared to conventional productoion systems.

A key differentiator in customer satisfaction in the automotive industry is offering a choice of high-dimensional possibilities to customize an individual vehicle. In the luxury segment this equals more than one billion variants. Innovative data-driven processes are necessary for the planning and handling of vehicles in production and distribution in order to guarantee indispensable factors such as stability, flexibility and transparency across the entire supply chain and to deliver the right vehicle to the right place at the promised time. Highly complex business environments, multi-variant products, trends with effects on distribution networks and future mobility concepts confront manufacturers with new challenges. This paper provides a survey of currently used methods and technologies to handle the previously mentioned challenges in the area of the customer order management of an automotive manufacturer. A specific field of research is the concept of planned orders which are derived from early anticipated customer requirements and their utilization throughout the entire planning and order management process. In addition to the generic approaches for achieving integrated planning of sales and production programs as well as the resulting material requirements, artificial intelligence methods are investigated with regard to the concept of the planned orders. For this purpose, various existing approaches to anticipate planned orders and to effectively manage the supply chain are examined. In conclusion, this paper provides a survey of state of the art methods regarding artificial intelligence linked to current research on agile production systems. Despite existing uncertainty in the upcoming years the implementation of stable data-driven processes is crucial. In this context, the possibility to prepare an automotive manufacturer for the upcoming challenges in digitalization and globalization is evaluated.

The goal of the Research Campus ARENA2036 is, based on excellent, interdisciplinary basic and applied research, to produce potentially disruptive and leap-frog innovations, to transfer them to industry, and thus to contribute to the active shaping of work, mobility, production of the future, and digitization. The seamless transfer of research results into industrial application is intended to increase the competitiveness of the business location Baden-Wurttemberg and to enable the creation of novel business models – especially for SMEs. An essential component here is the interdisciplinary and trans-institutional approach of various fields of science and application, which is reflected in the close cooperation of all actors under the umbrella of ARENA2036. Based on this basic idea, current research work is carried out in the field of data interoperability in the domains of product development, and production system design. The goal is to achieve a significant overall reduction of product development and market introduction times. This topic is particularly significant due to the currently ongoing transformation processes in the automotive industry, which question the prevailing product and production patterns and require an increasing flexibility of manufacturing processes. New product and production technologies have to be incorporated into serial production at ever shorter intervals, which poses great challenges for both product design and corresponding production systems.

The trend towards individualized products and the increasing demand for a greater variety of variants create new challenges for existing production environments and require a re-thinking of production. Established manufacturing systems that provide the desired flexibility are associated with significant productivity restrictions and are therefore unable to compete economically with production from rigid production lines. They are therefore often limited to serving niche markets. Consequently, an approach is needed that combines high productivity with high flexibility. For this purpose, this paper presents a new approach to manufacturing with an equally high productivity and flexibility, so-called value stream kinematics. The basic idea of value stream kinematics is to combine the advantages of specialized machines with the versatility of industrial robots. The vision behind this is to be able to realize entire value streams with uniform robot-like kinematics and no need for special machines.

Increasing flexibility in production systems is driving the use of robotic solutions. During their planning, robots must be placed according to their future operations. Thereby, influences such as space limitation, mechanical reach or cycle time must be taken into account. This paper introduces a concept based on the data farming methodology aiming at the optimal robot positioning for a given set of constraints. By simulating a defined sequence of robot operations with changing robot placement in a definable investigation area, each result data set is stored and analyzed. The simulation run with the best fitting robot position according to the defined key performance indicators is shown. For further evaluation, a clustering algorithm is used to evaluate the simulation results. The usage of the proposed method enables production planners to conveniently place robots in the optimal position according to their later application.

The current trend of mass customization pushes conventional production techniques to their limits. In the case of forming technology, limitations in terms of adaptability and flexibility emerge, while additive manufacturing lacks in the manufacturing of large, geometrically simple components. Combining both processes has potential to use the strengths of each process and thus realize time and cost efficient mass customization. As the interactions between the processes have not been fully investigated yet, in this work a distinct modelling approach in LS-DYNA is used to examine the influence of the additively manufactured elements on the formability. Namely, varying geometric properties and number of pins created with additive manufacturing are in the focus of this research. The used material is the alloy Ti-6Al-4V, which requires processing at elevated temperatures due to its low formability at room temperature. The results show a clear influence of the additively manufactured elements on the formability.

Manufacturing companies are confronted with increasing market and innovation dynamics resulting from the demand for differentiated products. This leads to increasing importance of flexibility and versatility as objectives of production, especially in final assembly, since the highest variant-induced complexity occurs there. For this reason and to avoid investment costs for product-dedicated assembly lines, companies strive to assemble several products on one assembly line in a so-called model mix. Highly iterative product development is increasing in order to take the mentioned volatile market environment into account. However, this agile development methodology contradicts the sequential processes of production development and the solution space already defined by the existing production system. In order to address this conflict of objectives, this paper proposes a procedure for a holistic and cross-domain development cooperation for the integration of a new product on an existing assembly line.

Global manufacturing companies are operating their manufacturing networks in an increasingly uncertain and complex environment. Consequently, they have to adapt their networks much faster to changing conditions. Thus, agile manufacturing networks that are able to react and adapt quickly at reasonable cost to those conditions are required. One of the main challenges in this context is the balancing of cost- and investment-intensive measures to increase flexibility and transformability on the one hand and the impediment of the overall changeability/agility by the associated increased capital tie-up on the other. Hence, this paper presents an approach to quantify and evaluate the benefits and costs of changeability measures. By systematically discussing a reasonable trade-off between costs and benefits, a dedicated, cost-effective agility for a manufacturing network is determined, enabling companies to improve their network design in terms of cost-effective changeability and thus ensure their competitiveness in the long term.

Flexible Job Shop Scheduling Problem (FJSSP) is a generalization of Job Shop Scheduling Problem (JSSP), in which an operation may be processed on more than one machine each of which has the same function. Most previous researches on FJSSP assumed that all jobs to be processed are available at the beginning of the scheduling horizon. The assumption, however, is always violated in practical industries because jobs usually arrive over time and cannot be predicted before their arrivals. In this chapter, Dynamic Flexible Job Shop Scheduling Problem (DFJSSP) with job release dates is studied. A heuristic is proposed to implement reactive scheduling for the dynamic scheduling problem. An approach based on Gene Expression Programming (GEP) is also proposed which automatically constructs reactive scheduling policies for the dynamic scheduling. In order to evaluate the performance of the reactive scheduling policies constructed by the proposed GEP-based approach under a variety of processing conditions, three factors, such as the shop utilization, due date tightness, problem flexibility, are considered in the simulation experiments. The results show that the GEP-based approach can construct more efficient reactive scheduling policies for DFJSSP with job release dates under a big range of processing conditions and performance measures in comparison with previous approaches.

The traditional manufacturing system research literature generally assumed that there was only one feasible process plan for each job. This implied that there was no flexibility considered in the process plan. But, in the modern manufacturing system, most jobs may have a large number of flexible process plans. So, flexible process plans selection in a manufacturing environment has become a crucial problem. In this chapter, a new method using an evolutionary algorithm, called Genetic Programming (GP), is presented to optimize flexible process planning. The flexible process plans and the mathematical model of flexible process planning have been described, and a network representation is adopted to describe the flexibility of process plans. To satisfy GP, it is very important to convert the network to a tree. The efficient genetic representations and operator schemes also have been considered. Case studies have been used to test the algorithm, and the comparison has been made for this approach and Genetic Algorithm (GA), which is another popular evolutionary approach to indicate the adaptability and superiority of the GP-based approach. The experimental results show that the proposed method is promising and very effective in the optimization research of flexible process planning.

Cooperation in the management and use of the Nile is becoming indispensable given the demographic challenges ahead. A prerequisite for effective cooperation is the establishment of a legal and institutional framework, which Nile Basin states have yet to agree upon. This chapter presents the overall conclusions of the book and highlights the advantages of close cooperation for all 11 Nile Basin states. After sketching the development and current status of international water law, it summarizes the Nile’s cooperative and treaty regime, noting in particular the limits of application of the 1929 and 1959 Nile water agreements. The 2010 Cooperative Framework Agreement provides a sound basis, but amendments would be necessary to reconcile the interests of all the riparian states and align the Agreement with the principles of current international water law. The principle of equitable use would provide valuable guidance for the countries to negotiate the allocation of uses and benefits of the Nile and build flexibility into the use regime, and it can durably strengthen their coordination and cooperation with regard to the Nile. Overall, this chapter concludes that developments in international water law have already had significant influence on Nile riparian states’ negotiating positions. Yet, it remains for them to find the political will to lay down a legal foundation for basin-wide cooperation along the Nile.

Running a sheer virtualized data center with the help of Virtual Machines (VM) is the de facto-standard in modern data centers. Live migration offers immense flexibility opportunities as it endows the system administrators with tools to seamlessly move VMs across physical machines. Several studies have shown that the resource utilization within a data center is not homogeneous across the physical servers. Load imbalance situations are observed where a significant portion of servers are either in overloaded or underloaded states. Apart from leading to inefficient usage of energy by underloaded servers, this might lead to serious QoS degradation issues in the overloaded servers.

The need to modernize the electricity networks is based first and foremost on the integration of more sustainable generation resources, especially the partially volatile renewable sources. The volatile character which is due to significant shares of renewable sources (wind and sun) has a crucial influence on the operational behavior of the overall power system. Consequently, it is necessary to significantly enhance the flexibility and smartness of the electricity generation processes. Smart Generation concerns the complex coordination of volatile and definitely controllable power plants, storage capabilities and demand side management facilities. The advanced generation and storage technologies are described. Both the Smart Grid and Smart Generation concepts will ensure the reliable, sustainable and environmentally friendly electric power supply of the future.

The change in production towards smaller batch sizes demands a high degree of flexibility, especially in assembly. In this context, human-robot collaboration (HRC) is becoming increasingly important. The goal is not to displace the human being by the robot, but instead receiving support from it. Nevertheless, employees at all levels have to be prepared for the HRC. Trainings offered from robot manufacturers focus on robot specific operations and are not sufficient for the development of competencies for innovative and creative process planning and optimisation. Therefore, a mixed reality learning environment can be used for the implementation of such training, since real production facilities are only available to a limited extent for training purposes. In the context of the implementation of a mixed reality learning environment, the question arises as to the necessity of haptic feedback. This article presents a concept for integrating HRC in German initial and continuing vocational education and training using mixed reality.

Innovating maintenance is crucial for the competitiveness of the European manufacturing, pressured to increase flexibility and efficiency while reducing costs. Initiatives related to Industrie 4.0 have been showing the potential of using advanced/pervasive sensing, big data analytics and cloud-based services. In this paper, we present the edge part of our solution for self-healing manufacturing to early-predict equipment condition and make optimized recommendations for adjustments and maintenance to ensure normal operations. The intelligent edge is advanced, affordable and easily integrated, cyber-physical solution for predicting maintenance of machine tools in varying manufacturing environments, by using new connectivity, sensors and big data analytics methods. The proposed solution is capable to integrate information from many different sources, by including structured, semi-structured and unstructured data. The key innovation is in IoTization through dynamic, multi-modal, smart data gathering and integration based on the semantic technologies.

Overview Energy storage systems (in the past as well as today) are one significant part in the energy supply. The following three chapters describe how storage demand will develop in the future for the electricity, heat, and traffic sectors, as well as for non-energetic consumption of fossil resources (the chemical industry). Chapter 3, the core of this section on storage demand, makes clear how and why the electricity sector is the nucleus of the energy supply of all sectors and why it creates essential bridges between electricity, heat, and transport sectors, as well as with the chemical industry. If planned electricity network expansion takes place and flexibilities in generation and consumption are fully exploited, the demand for electricity storage, according to present estimates, will only reach a significant scale at 60–80% shares of renewable energy in the power supply. Network expansion has a great impact on the storage demand, as well as flexible power generation in power plants, combined heat and power (CHP), and flexible consumption via demand-side management (DSM). Four studies in the context of storage demand and the role of energy storage systems for flexibility are comprehensively addressed. The authors and the co-authors were themselves participants in these studies, which will be complemented by ongoing research. A meta-study summary of the main results is shown in Abschn. 3.7, and these results are compared with seven further studies.

Business Process Management (BPM) is a central element of today’s organizations. Over the years, its main focus has been the support of business processes (BPs) in highly controlled domains. However—in the current era of Big Data and Internet-of-Things—several real-world domains are becoming cyber-physical (e.g., consider the shift from traditional manufacturing to Industry 4.0), characterized by ever-changing requirements, unpredictable environments and increasing amounts of data and events that influence the enactment of BPs. In such unconstrained settings, BPM professionals lack the needed knowledge to model all possible BP variants/contingencies at the outset. Consequently, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents). In this context, automated planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviors in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for solving concrete problems in the BPM field that were previously tackled with hard-coded solutions. To this aim, we first propose a methodology that shows how a researcher/practitioner should approach the task of encoding a concrete problem as an appropriate planning problem. Then, we discuss the required steps to integrate the planning technology in BPM environments. Finally, we show some concrete examples of the successful application of planning techniques to the different stages of the BPM life cycle.

Industrial assembly systems, in particular for large scale products manufactured in lot size 1, require increasing levels of flexibility and (re-)configurability to shorten response times in volatile market environments thus maintaining competitiveness. Expanding on existing approaches for flexible production systems, we introduce the organizational and technological principle of Line-less Mobile Assembly Systems (LMAS) by combining the principles of dynamic job routes and mobile resources. Beside product-station assignments and schedules, operations planning for LMAS requires the determination of favorable assembly locations on the shop floor. Consequently, we introduce a linearized multistaged cost-based decision model for LMAS operation.

In modern manufacturing industry, the factors of competition cannot be reduced to a small number. Although the most obvious ones seem to be time and costs, over the years, flexibility became an aspect of manufacturing not to be underestimated. One way of providing the mentioned flexibility was to use servoelectrical machines as drives for forming presses in the beginning of the century [1]. A higher efficiency, a higher output rate or an adjustable ram stroke profile are just some of the advantages this technology brings over conventional forming presses [2,3]. The design of forming presses with servo drives presents certain challenges. As the torque of the servo drive depends on its size, a change in the dimensions of the coupling leverage also affects the installation space needed for the drive. On counter, the installation space probably restricts the dimensions of the coupling leverage. This correlation potentially leads to a big number of iterations with suppliers in the overall design process that requires time and money. In this paper a software tool is introduced which speeds up the design process for forming presses by providing a preliminary design of the servo drive to estimate the drive’s installation space and other parameters in advance. Hereby, the development process also becomes more adaptive and the forming press manufacturer is more responsive to customisation [4], which gives an advantage regarding flexibility. By saving time and costs, the tool also allows press manufacturers to increase their productivity to higher limits.

SFA GmbH & Co. KG from Aichstetten specialises in CNC milling/turning and custom machine construction. The special-purpose machines are developed and assembled entirely by SFA, from the design through to mechanics and electrics. They also offer the related software development and service. In order to be able to produce several different parts with the shortest possible changeover times practically simultaneously and to achieve the best possible customer satisfaction, SFA wanted a highly flexible, automated production cell.

The current trend towards mass customization confronts established mass production systems with specific challenges, as these are designed for the production of large quantities with a small number of variants. Future production layouts will have to provide a high degree of flexibility and changeability in order to be able to respond to the demand for individualized mass products. This leads to a production network in which the work schedule of each product and thus the sequence of the individual production resources are flexibly arranged without losing the cost advantages of mass production. The current design forms, as a monolithic AGV system or the use of flexible autonomous units, show specific weaknesses that limit the use of these solutions in a flexible production network. The paper describes the research approach to establish an intralogistics service-based software architecture as an intralogistics execution system, which contains the elementary functions of a cross-platform use of mobile autonomous systems. One key application is the implementation of typespecific routes, which enables the cross-platform digitalization of the workspace to improve the interoperability and efficiency in route and path planning of different transport solution within one scenario. The implementation of the approach shows the advantages of a cross-platform service structure in general and the routing service in particular.

The concept of Lean Production Systems is an approved and widely used approach in enterprises, which gain efficient processes through avoidance of waste on the shop floor. Especially German enterprises reached a high degree of maturity regarding the implementation of Lean Production Systems. Another, newer concept is Industry 4.0. Industry 4.0 is one of the most promising approaches to handle future production problems. For example, the flexibility of the production system resulting from Industry 4.0 helps manufacturers to cope with the high demand for customized products. However, Industry 4.0 entails deep technical and organizational changes for production systems, that may affect a Lean Production System. It needs to be considered that there are not only positive combinations of Lean Production Systems and Industry 4.0. It is unclear, how both concepts can be operated together and which methods are useful to fulfill the aims of a modern production system. The paper addresses this issue by presenting the development of a method library for the combination of the elements of the Lean Production System and technologies of Industry 4.0. For this purpose, the reciprocal effects of the Lean Production System and the Industry 4.0 technologies are examined regarding the scope of production. These results are the foundation of the method library where all combined methods are consistently presented.

The digitalization of the process of foot orthosis design and manufacture has been a significant development in the field of lower limb injury management. While there is a significant benefit in improved speed and efficiency, it is the increased flexibility and individualization of the design process that leads to improved clinical outcomes.

With the progressive implementation of Industry 4.0, many established manufacturing processes are qualified to meet new market requirements such as shorter product life cycles, smaller batch sizes and increased flexibility. Due to complex adjustment tasks, roll forming requires long set-up and troubleshooting times. The recording of process characteristics gained by sensor networks contributes to an improved understanding of the process. The evaluation of monitored data using machine learning allows for faster error detection, which in turn reduces costs and increases overall equipment effectiveness as well as product quality.

During the last decade, customer expectations have shifted more and more towards individualized products. Producing companies often reacted by offering more and more variants of their products. That has led to declined series quantities. Hence, companies frequently re-organized their formerly cost-oriented flow production structures into workshop production structures aiming at higher flexibility, which generally comes at the cost of increased complexity. A high planning quality is a pre-requisite to cope with this complexity economically. One reason for today’s often inaccurate planning are scattering lead times that consist mostly of times between operation steps, so-called transition times. Typical approaches for a prediction of these times, such as estimation by experts or calculation of historical mean times, cannot handle the complexity sufficiently. Hence, an approach based on data mining methods was developed. However, using order-specific prediction models to improve forward planning requires continuous monitoring and maintenance. In order to support the implementation in practice, this paper presents a suitable control loop concept.

Management information systems of distributed nature, play a vital role in any kind of business organizations’ activity. The multi-agent systems, based on cognitive agent architecture, deserve special attention in this class of systems. They allow not only to access to the information and quick search for interesting us information, its analysis and drawing conclusions, but also, in addition to responding to stimuli from the environment, have the cognitive ability to learning through empirical experience gained through direct interaction with the environment. It, in turn, allows for the automatic generation of variants of decisions and, in many cases, even taking and putting into action the decisions. The big problem currently, however, turns out to be the processing of unstructured knowledge in systems of this kind. In contemporary companies, unstructured knowledge is essential, mainly due to the possibility to obtain better flexibility and competitiveness of the organization. Therefore, unstructured knowledge supports structured knowledge to a high degree. Simultaneously, one must note that the most prevailing phenomenon is a conflict in unstructured knowledge. It is extremely difficult to resolve conflicts of this kind properly. However, it is also very important, since it can improve the operation of management information system and, consequently, help the organization that employs the system become more flexible and competitive.

Planning of deep brain tumor biopsy is a time intensive task and the result highly dependent on tumor position and patient individual anatomy. The decision on the best needle trajectory is generally based on expert knowledge on optimal entry points and angles as well as trajectory length and rigid rules in respect to avoidance of and safety margins to risk structures. The increasing availability of more detailed data on brain anatomy further increases the complexity of the planning task. However, current computer supported planning systems generally work with fixed rules and a limited set of structures at risk. We propose BrainXPlore, a visual analytics based planning tool allowing neurosurgeon to interactively explore and refine the space of possible trajectories in the context of different quality measures and to define custom rules. To ensure interactivity and performance even for a high number of anatomical structures, we employ a spatial index allowing to access distance information for trajectories in real time. We evaluated BrainXPlore on real brain biopsy planning tasks and conclude that our system can decrease the time needed for biopsy planning and aid novice users in their decision-making process.

Appropriate assistive systems are used due to the demographic change, increased temporal and spatial interaction or integration of human and robots as well as necessary flexibility in respect to tasks and location. Especially safety and flexibility for effective human-robot-interaction and a broad spectrum of tasks are important. Existing technologies use different kinds of mechanisms or principles for realizing safe interaction, especially e.g. soft control, optical approaches and sensor skin in combination with conventional industrial robots. In addition, the field of soft materials robotics is becoming increasingly important, with the focus to increase E-modulus of structure elements. This paper presents an approach for soft and self-unfolding modular robots on the basis of paper lamella technology which can be used as a system for human-machine interaction as well as for robots with the ability of self-unfolding and -folding. For demonstration, a realization of a self-unfolding lamella element as well as the results of first measurements are summarized.

Varying sales expectations, shorter product life cycles, and a rising product variability caused by individualization: Manufacturing companies are facing the challenge of continually adapting their assembly systems to these and other dynamic conditions. The dynamic interconnection of stations enables to design flexible and individual assembly sequences for each product. Benefit is the possibility to assemble customer-specific products and to react to dynamic conditions of the system. This paradigm shift entails a raise in the level of complexity and flexibility in terms of coordinating the different product flows, exceeding the possibility of existing control systems. Within this paper, we therefore present the concept of a service-oriented communication and control system architecture as a solution to cope with the introduced challenges and requirements of Dynamically Interconnected Assembly Systems.

The rising demands of the markets with regard to product individualization and innovative technology deployment force companies to shorten product life cycle times and to use more variants in their production process. To get this flexibility, the employees in the assembly area need to be qualified and supported in an appropriate way. This work presents a concept to support workers during the assembly of products using augmented reality. Head mounted devices are used to demonstrate the correct assembly sequence of the product. The presented data is based on the 3D-CAD models of the product. The different types of parts as well as the positions are detected with the use of optical markers. In that way, changing conditions in the production can be considered. An intuitive interface is implemented to give the worker the opportunity to change program settings tailored to his requirements.

IT-based systems used in the manufacturing environment, such as MES, must satisfy special requirements, which among other things also have consequences for the system structure. These include, for example, requirements arising from the horizontal and vertical integration we have described, from the production organization to be mapped, from availability requirements, from ergonomics and ease of operation, or even from difficult environmental conditions at the machine and system. Rigid MES systems do not offer enough flexibility to be able to adapt with reasonable effort to the boundary conditions given, since every instance of production is unique.

Modern industrial settings require great flexibility of systems. For example, in automated factories, smart cities or smart grids, systems are exposed to highly variable and dynamic environments: Not all events can be modeled at design time, system components may fail to operate as desired at runtime, and stakeholder requirements may change at high frequency. In order to handle these challenges, systems have to expose highly flexible behavior. Due to the complexity of application domains, deterministic specification and validation of system behavior and quality is no more feasible. Instead, new approaches for building flexible systems yield probabilistic behavior at runtime. While this defeats the purpose of design time testing and quality assessment, it is indeed possible to shift the standard design time engineering activities towards runtime. This enables monitoring and validating system behavior at runtime, incorporating information not available a priori and using it to keep the dynamic system under control at runtime.

This article is about an innovative welding system for the fully mechanized production of large dimensioned tubular nodes which are designed as welding constructions. The system is characterized by its flexibility regarding to the weight of the components as well as the type of installation. First, the main assemblies of the robot welding system and their essential components are described. Problems concerning the welding process as a consequence of the design concept are presented. Additionally, solution approaches to these problems are discussed on the basis of experimental results. Finally, a description of potential process steps within the production of tubular nodes, which also could be implemented by the use of the described manipulator, is given.

When a firm imports inputs from foreign countries, the management faces two options: buying from unaffiliated firms or insourcing the foreign production. This paper suggests that this decision directly affects domestic production because international insourcing affects the operational flexibility and the firms’ opportunities for accessing knowledge and capabilities developed abroad. Empirical results based on firm-level data of Swiss firms confirm this hypothesis. Concretely, the insourcing of international production increases domestic productivity, decreases (at least in the short run) domestic employment and possibly investments. In line with transaction cost literature, we observe that contractual hazards moderate these effects.

Recent advances in safe human-robot coexistence make collaboration of humans and robots in achieving common goals feasible. We propose a concept that treats human and robot agents as equal partners in executing a task specified by a shared task model. Equality between agents offers high flexibility, as e.g. the team composition may change arbitrarily without interrupting the working progress. The main challenge in achieving flexible teaming is coordinating the robot with operations executed by human partners. We contribute an approach to this problem that is based on observing pre- and postconditions of operations using a robot-mounted camera system. The coordination mechanism is embedded into a framework that allows domain experts to create, test, and dispatch new task models to collaborative execution. The approach is validated by experimental tasks composed of pick-and-place operations.

In this chapter the proposed concept to increase flexibility of static TTEB INSs is evaluated. Since the proposed approach consists of three main contributions described in chapters 3 - 5, the evaluation will be also divided into three main parts. However, without loosing the validity of the whole approach.

This thesis presented an introduction to complexity management in engineering design. The beginning provided some relevant basics about complexity in the context of engineering. Next a historic perspective of the emergence of system thinking and complexity was presented. This was succeeded by a categorization of recent research fields working on complexity and its management. The purpose of this depiction is to motivate the discussion about the state of the art and the future relevance of topics. The world gets more and more networked and with it the recent global challenges become more complex. Following the ideas of bio-inspired system design and considering that biological systems have reached states of robust complexity with impressive functionality and flexibility, one can argue that the intentional design of complexity for targeted engineering of powerful functionalities is the future of complexity management.

The world of work and business is constantly changing. At the moment, the digitalisation megatrend is significantly changing framework conditions for companies with a range of new requirements. Within this context, a growing desire for more flexibility, which can be achieved thanks to new developments in information and communication technology, can be seen both at companies and among employees. To ensure the long-term success of digitalisation at companies, work must be organised with a human-oriented focus in all operating areas. Human orientation has both a direct as well as an indirect impact on productivity. For efficient human-oriented productivity management, new methods and tools in industrial engineering are required, which would allow the digitalised working world to be analysed and shaped.

Agent Swarm Optimization is a framework that combines the use of evolutionary algorithms, data mining, modeling and other techniques to find the best compromises among objectives in complex decision problems. It has been applied mainly in engineering cases where using classic optimization algorithms would require undesired simplifications of the problem or the use of simulators for evaluating the objective functions. The flexibility of evolutionary algorithms makes possible to use them in practically any case. Nevertheless, in this paper we are presenting a complex problem where using “pure” evolutionary algorithms was not resulting in good solutions. A different situation appeared after using rules for reducing the search space and moving the evolutionary process toward zones with a higher probability of containing good solutions. The results of using rules is also presented in this paper for the case studied. Additionally, the paper explores the capacity of the algorithms to discover additional rules that can improve the search process and the way the evolutionary algorithms behave in problems where the expert knowledge to generate search rules is limited.