This paper reviews the current state of literature for controlling automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) in a factory considering battery management (BM). The optimal allocation of tasks to a fleet of mobile robots known as multi-robot task allocation (MRTA) problem is one of the major problem classes covered in research. The limited capacity of the mobile power source and necessary charging or battery swapping operations are often neglected. This effectively limits the availability of the individual vehicles. If a high number of vehicles are charging at the same time in a state of high demand this will have a significant impact on the performance of the whole transport system. The impact of charging can effectively be eliminated by increasing vehicle count. In practise this often results in over dimensioned systems rather than focussing on a more sophisticated BM strategy. For economic and sustainability reasons this cannot be considered a good approach.

This paper aimed to identify existing approaches of automated risk management procedures for companies in their supply chains and to present an approach for automated risk analysis based on textual news using artificial intelligence. Methodologically, a structured literature review was carried out in the databases "Web of Science" and "Scopus", in which the relevant results were categorized by the aim of the presented approaches on the basis of the records’ abstracts. Four approaches were categorized as “Textual risk evaluation using AI/LLM” and were presented in process flow diagrams. Including learnings from these approaches, a new framework for an automated risk analysis was developed. Further research efforts are recommended for optimization in the area of process models and prompt engineering for automated risk analysis.

Recent large-scale disruptions to global supply chains —such as the COVID-19 pandemic, the blockage of the Suez Canal, and economic sanctions against Russia —have severely impacted production, causing delays, shortages, and substantial financial losses. These disruptions often originate from specific events but propagate across entire supply networks, amplifying their consequences. This paper identifies gaps in existing literature and highlights structural deficiencies in current resilience approaches for supply chains. It emphasizes the need for precise, quantitative metrics to define resilience and assess disruption severity. To address these challenges, a stochastic model for aggregate production planning is introduced, designed to mitigate large-scale disruptions. The model is then tested through a case study involving a real-world supply chain exposed to high disruption risks derived from a historical data set, providing an assessment of its effectiveness.

The recent years have shown a high frequency of disruptions like natural disasters or manmade disruptions, leading, for example, to transport routes or production facilities being unavailable for extended periods. Many manufacturing companies, which nowadays operate in a global production network, are heavily exposed to these disruptions. This results in both significant costs and substantial exceeding of promised deadlines. If it concerns the first company in a supply chain, it causes correspondingly significant delays in promised deadlines for subsequent companies in the supply chain, including a correspondingly significant increase in costs. Both are exemplified in this paper through a case study. This case study demonstrates that through resilience, both implications can be significantly reduced.

Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are being applied more and more frequently in a wide range of use cases. Following the general trend of decentralisation of control structure in the Industry 4.0 paradigm, this paper analyses the application of auction algorithms to solve the robot task assignment problem. We focus on the use of mobile robots in production environments with a high level of uncertainty which places hight demand on the flexibility of the online scheduling architecture. A sequential single-item (SSI) auction algorithm is validated for a real-world use case where multiple mobile robots supply car bodies to manual rework stations in a paint shop application using simulation. The optimization objective of such an algorithm is discussed in regards to the examined use case.

Against the background of the energy crisis, shortage of skilled workers, demographic change and other drivers, sustainable development is also becoming increasingly important for industrial companies. In this respect, production planning and control has an enormous influence on relevant objectives. In classical approaches of production planning and control as presented in this paper, economic-oriented objectives are taken into account to a large extent in decision making. This paper demonstrates that besides these classical models, a variety of approaches exist to influence ecological and social targets through production planning. These different models are assigned to sustainability areas and outlined by exemplary literature sources. Further research is needed, for instance, in the joint consideration of sustainability criteria along different planning levels and sustainable dimensions.

This article describes primary findings of various simulation runs on job shop scheduling dealing with energy consumption and emission pollution. By two combinations of priority rules, production is linked to the generation output of a renewable energy source installed onsite. The resulting schedules show a reduction of energy-related emissions and makespan compared to several conventional priority rules often used in industrial practice.

Flexibility and in particular volume flexibility is an important topic for industrial manufacturing companies. In this context, the harmonization of the available and required capacity is a central task, especially with increasing fluctuations in customer demand. In classical approaches, this is considered only by the use of additional capacities and there are only a few approaches that combine aspects of personnel planning with production planning. Therefore, this article presents a linear optimization model for master production scheduling that includes aspects of personnel requirements planning. It is used to investigate different strategies for the use of overtime and temporary workers in order to achieve different levels of volume flexibility. With regard to the monetary and social impacts, the results indicate that overtime has a stronger influence to achieve volume flexibility than the use of temporary workers. However, both are affected by substantial deficits in human working conditions. But the results also imply a promising potential for improving the social aspects without a significant increase in costs.

This article reviews the state of the art in research regarding sustainable extensions of hierarchical production planning. Sustainability is currently of considerable importance due to various interest groups. Hierarchical operational production planning and control is the state of the art in research as well as in industrial practice for planning of stations and their aggregations to production systems. Thus, it might be highly relevant to improve sustainability. In the literature mainly the scheduling level as well as the ecological dimension are considered. So the current research is limited to selected partial planning problems and incomplete with regard to sustainable aspects that emerge.

Local search is an established meta heuristic for scheduling problems. To avoid of getting stucked in a local optimum, the achieved solution is destroyed. Usually, this is done randomly. Here, two problem specific procedures are suggested. One focuses on jobs with the highest difference between the actual processing time and the net processing time of the job and the other one tries to remove jobs which causes the highest idle time for themselves or other jobs. Comprehensive simulations of test scheduling problems as well as a real world application show that both problem specific procedures outperform a random procedure.

In capacitated production systems at high utilization there exists a nonlinear relationship between the orders which are in process and the output. This nonlinear relationship can be described by nonlinear Clearing Functions. We show how a Clearing function will be estimated and integrate it into a model of order releases planning. We compare our model with two inventory management policies under different demand conditions.

In this paper, we report on work done evolving Bayesian Networks with Genetic Algorithms. We use a Chain Model GA [19] to induce a Bayesian network model for the real world problem of Prostate Cancer management. Bayesian networks can and have been used in a wide range of complex domains, notably in medicine. In fact, they have shown powerful capabilities in representing and dealing with the uncertainties generally inherent in the clinical practice. In this study, we investigate those capabilities by testing the evolved model's predictive power and exploring its potential use as a more versatile alternative to the widely used Partin tables for prostate cancer pathology staging.

Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]. The super exponential growth of the number of possible networks given the number of factors in the studied problem domain has meant that more often, approximate and heuristic rather than exact methods are used. In this paper, a novel genetic algorithm approach for reducing the complexity of Bayesian network structure discovery is presented. We propose a method that uses chain structures as a model for Bayesian networks that can be constructed from given node orderings. The chain model is used to evolve a small number of orderings which are then injected into a greedy search phase which searches for an optimal structure. We present a series of experiments that show a significant reduction can be made in computational cost although with some penalty in success rate.