Dynamics in Logistics

Manufacturing and logistic service companies are increasingly confronted with high dynamics and complexity. Due to its particular suitability for short-term and situation-dependent decision-making, autonomous control can improve planning and control of production and related transportation processes. This chapter gives an overview of the research that the BIBA—Bremer Institut für Produktion und Logistik GmbH has performed over the past years in the field of autonomously controlled production and transportation networks. The chapter focuses on the modeling approaches and the autonomous control methods that have been developed. These methods have been evaluated using both theoretical and real-world scenarios. The results show the applicability and suitability of autonomous control in complex and dynamic production and transportation environments. In addition, influences on the methods’ performance and the integration of autonomous control into conventional planning and control systems are discussed. Finally, the chapter looks at the significance of autonomous control in the context of Industry 4.0 and shows the relations between both concepts.

Decision-making under uncertainty is a major challenge in logistics. Mathematical optimization has a long tradition in providing powerful methods for solving logistics problems. While classical optimization models for uncertainty in the input data do not consider the option to actively query the precise value of uncertain input elements, this option is in practice often available at a certain cost. The recent line of research on optimization under explorable uncertainty develops methods with provable performance guarantees for such scenarios. In this chapter, we highlight some recent results from the mathematical optimization perspective and outline the potential power of such model and techniques for solving logistics problems.

The methodology to model systems as graphs or networks already exists for a long time. The availability of information technology and computational power has led to a renaissance of the network modeling approach. Scientists have collected data and started to create huge models of complex networks from various domains. Manufacturing and logistics benefits from this development, because material flow systems are predetermined to be modeled as networks. This chapter revisits selected advances in network modeling and analysis in manufacturing and logistics that have been achieved in the last decade. It presents the basic modeling concept, the transition from static to dynamic and stochastic models, and a collection of examples how network models can be applied to contribute to solving problems in planning and control of logistic systems.

Predictions such as forecasts of congestion effects in transportation networks can be based on complex simulations that include many aspects of actual transportation systems. On the other hand, rigorous mathematical traffic models give rise to theoretical analyses, very general statements, and various traffic optimization opportunities. There has been a huge development in the last years to make mathematical traffic models more realistic. This chapter provides an overview of the mathematical traffic models developed recently and some state-of-the-art results.

The digitalization of logistics processes is often based on distributed models and decentralized control. As these logistics models constitute an important part of Industrie 4.0 concepts they must be powerful enough to cover dynamic processes and must enable a host of functions such as goal-oriented, reactive, pro-active, communicative, cooperative, competitive, and learning behaviors. In addition, these distributed models must allow for simulating, planning, allocating, scheduling, and optimizing logistics tasks. This implies that they must be able to act through communication channels with each other thus establishing logistics social communities.

Multiagent Systems (MAS) have been around for more than 30 years and lend themselves to the implementation of these distributed models needed for autonomous and cooperating logistics processes. It will be described and also demonstrated by three case studies why MAS are well suited for social and learning logistics systems. It will be shown how the resulting distributed MAS models provide the required functionalities for production and transportation logistics including the handling of dynamic local events as an essential feature for the successful planning, scheduling, optimizing, monitoring, and control of global logistics processes.

A Semantic Mediator was conceived in the CRC 637 (The Collaborative Research Centre 637 “Autonomous Cooperating Logistic Processes” focused on adaptive logistic processes including autonomous capabilities for the decentralised coordination of autonomous logistic objects in a heterarchical structure.) to tackle problems of interoperability of heterogeneous information sources in autonomous cooperating logistics processes. Since the conclusion of the CRC 637, the Semantic Mediator has been developed further and successfully transferred to interoperability problems in different domains, including Industry 4.0, the Internet of Things, and Product Lifecycle Management. This paper will introduce the Semantic Mediator and present examples of its successful application.

As digitization advances, stationary retail is increasingly enabled to develop novel retail services aiming at enhancing efficiency of business processes ranging from in-store logistics to customer shopping experiences. In contrast to online stores, stationary retail digitization demands for an integration of various data like location information, product information, or semantic information in order to offer services such as customer shopping assistance, product placement recommendations, or robotic store assistance.

We introduce the semantic Digital Twin (semDT) as a semantically enhanced virtual representation of a retail store environment, connecting a symbolic knowledge base with a scene graph. The ontology-based symbolic knowledge base incorporates various interchangeable knowledge sources, allowing for complex reasoning tasks that enhance daily processes in retail business. The scene graph provides a realistic 3D model of the store, which is enhanced with semantic information about the store, its shelf layout, and contained products. Thereby, the semDT knowledge base can be reasoned about and visualized and simulated in applications from web to robot systems. The semDT is demonstrated in three use cases showcasing disparate platforms interacting with the semDT: Optimization of product replenishment; customer support using AR applications; retail store visualization, and simulation in a virtual environment.

The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.

Digitalization and the transformation of industry into Industry 4.0 is changing the character of production logistics substantially. New Logistics 4.0 technologies are largely enabling automated decision-making by machines. Human decisions are nevertheless still required. Research shows, however, that human decisions are often more biased and less rational than most logistics models assume. Decision makers and decision support system designers therefore need to understand the influence of the so-called cognitive biases on the human decision-making process. We contribute to the scholarship on this issue by combining the literature streams of logistics and cognitive biases. We demonstrate the influence of cognitive biases on human decision-making based on typical decisions in logistics and derive initial hypotheses.

In today’s buyer’s markets, logistical service quality is of great importance, particularly for high-priced products. For many buyers, the automobile is the epitome of a high-priced, individually customized product. Hence, customers expect a high delivery service quality. Distribution logistics provides the link between the manufacturer and the customer and is therefore responsible for providing these services. In automobile logistics, the distribution chain involves several stakeholders, such as manufacturers, transport, and technical services providers, as well as dealers. Seamless coordination between them is crucial. Digitalization is an inevitable means for gaining a high level of transparency among the stakeholders. In addition, it provides technologies for developing effective and tailor-made assistance and control systems that support processes along the distribution chain. In recent years, the BIBA—Bremer Institut für Produktion und Logistik GmbH has been active in automobile logistics research and has been involved in developing several assistant and control systems that range from relatively simple track and trace systems to highly complex compound control systems. In this paper, we present an overview of the developed systems and provide a vision, how recent and ongoing research improves automobile logistics and how to further potential can be leveraged.

Food losses in the cool chain, which are mostly caused by temperature deviations, can be reduced by remote monitoring of transport conditions. The project ‘Intelligent Container’ was begun 15 years ago to provide the necessary sensor system, communication and automated evaluation of data. If transport and delivery planning are adjusted according to the actual quality or the predicted remaining shelf life, more products arrive with sufficient quality at the customers. This paper summarizes the project results and highlights current trends in industrial application and research, such as commercial remote container monitoring and standards for data exchange, sub-GHz communication, the increasing availability of biological and computational fluid dynamics models and digital twins. Open research topics include the development of specialized sensors. To overcome obstacles hindering the industrial application of sensor quality monitoring, we suggest a gradual approach, with lower company resources required for the first action points. Food losses can be reduced, even if the complete system, including permanent remote access and adaptive stock rotation, is not applied.

The development of the global container fleet has followed a clear trend towards ever larger ships over the last 25 years. Particularly striking in this regard is the rise of the dimensionally largest ships, the so-called Ultra Large Container Vessels or ULCVs that can no longer pass through the new locks of the Panama Canal. While recent events such as the six-day blockade of the Suez Canal by the Ever Given have revealed environmental and safety risks of deploying these vessels, even the scheduled and smooth operation generates a whole range of challenges, impacts and costs that come at the expense of external stakeholders. The article aims at identifying these external effects as related to seaport systems as well as environmental considerations by consolidating insights from the scientific and professional discourse.

The online grocery trade has received an additional boost from the Covid pandemic. The delivery of such purchases places particular demands on last mile logistics since consumers demand more and more individualized delivery options, e.g., regarding the delivery arrival or the type of transport. At the same time, many consumers are becoming more environmentally conscious, so there is a need to examine further how this particular consumer behavior affects the sustainability of deliveries. This paper develops and presents a simulation model, which considers grocery delivery under different framework conditions. The examined scenarios show that a change in consumer behavior directly impacts last mile logistics systems, mainly by increasing the total number of orders and a slight reduction in emissions through improved vehicle utilization. Nevertheless, the results show that without sufficiently high utilization of delivery vehicles, shopping trips by private car may cause fewer emissions.

In 2013, Chinese President Xi Jinping advised to establish the “Silk Road Economic Belt” and the “21st Century Maritime Silk Road,” also referred as the Belt and Road Initiative or the New Silk Roads Policy. The intention is to promote international and regional trade as well as cooperation in and between Asia and Europe. Consequently, international maritime and terrestrial freight transport corridors are either established or strengthened and operated. The purpose of this paper is to reflect the Belt and Road Initiative from the perspective of logistics. The aim is to identify and formulate circumstances, expectations, opportunities, and peculiarities of logistics along the New Silk Roads. For this purpose, four corresponding challenges will be considered and outlined after an introduction to the Belt and Road Initiative. The four logistics challenges concern the awareness of new freight transport corridors and the assessment of possibilities for opening new transport relations and new markets, the implementation of new and the adaptation of existing supply chains to increase strategic logistics flexibility, the availability and use of digital infrastructure and connectivity for improved communication and coordination of logistical processes, and the willingness to consider regional and cultural differences in the preparation and realization of supply chain decisions.

Logistics is a global challenge needing cooperation across disciplinary as well as cultural diversity. International, interdisciplinary education has the potential to provide meaningful experiences and cultural exchange for the individuals involved, including students, faculty, and university professionals and can have a positive impact on their lives and professionalism. We introduce the doctoral training program International Graduate School for Dynamics in Logistics (IGS) at the University of Bremen and explores a German-American educational partnership on the level of doctoral training. Utilizing scholarly personal narratives, the perceptions of two university professionals were garnered. From the narratives, they discussed the advantages and difficulties associated with cultural exchanges, the exposure to many cultures, and engaging with differing educational systems converging within one program. This qualitative study provides and glimpses into the complexity and intentional nuances that should be addressed when developing and operationalizing international, interdisciplinary education.