Research Projects

The project focuses on the optimization of production processes in the aerospace industry, particularly on the planning and control of manufacturing orders. The core of the research lies in the development and analysis of mathematical models that integrate both classical production objectives—such as efficiency and cost minimization—and regulatory as well as safety requirements. A special emphasis is placed on accounting for stochastic influences in order to increase the robustness and validity of planning decisions under real-world conditions. The aim of the research is to develop innovative methods that help manufacturers optimally design complex production processes while considering uncertainties, legal requirements, and quality standards.

View project in the research portal

The demand for home healthcare continues to rise. In the light of ongoing nurse shortage and the aging population, home healthcare service providers face growing challenges in providing reliable on-site healthcare.
In collaboration with a regional health insurer, we examine the potential of innovative approaches to serve and distribute the numerous patient inquiries. At first, we assess the savings through systematic patient redistribution between home healthcare providers in the daily nurse routing. Next, the goal is to investigate how patients can be incentivized to participate in these voluntary exchanges and how providers can be compensated for potential patient losses. At a strategic level, we explore how to establish a (policy) framework that enables and regulates patient exchanges. As working time has become one of today’s most valuable resources, reducing travel times through smart patient-to-provider assignments offers significant potential to enhance efficiency. To ensure service continuity in the future and to make effective use of scarce resources, innovative solutions are essential.

By integrating real-world data, this project evaluates the practical benefits of patient exchanges. Specifically, we examine potential reductions in travel time and corresponding improvements in the utilization of scarce nursing resources, which may ultimately enable more on-site care. Furthermore, we analyze various exchange mechanisms and incentive structures for both patients and providers to identify the most effective and equitable approaches for all stakeholders involved.

View project in the research portal

The goal of this project is to systematically improve quantitative decision support for urban mobility and logistics, to analyze its methodological functionality, to derive general conceptual insight, and to use the derived concept for future method designs.For applications in urban mobility and logistics, operational decision support needs to be effective, fast, and applicable on a large scale - often under incomplete information. Providers face uncertainty in many components, for example, the customer demand, the urban traffic conditions, or even the driver behavior. Mere adaptions to new information are often insufficient and anticipation of this uncertainty is key for successful operations. In research and practice, a range of anticipatory methods has been developed, usually tailored to specific practical problems. Such methods may follow intuitive rule-of-thumbs, draw on sampling procedures, or use reinforcement learning techniques. While the methods may perform well for individual problems, there is still a very limited understanding of the general dependencies of a method’s performance and a problem’s characteristics. This research project will provide this conceptual understanding.To this end, the project will systematically develop and compare different methodology for a set of problems from three different application areas, one combining urban mobility and transport as a service, one using a network of parcel stations for urban transportation, and one performing pickup and delivery with a gig economy workforce. The three problems differ in several dimensions, especially in their sources of uncertainty. To classify the problems, measures will be developed, for example, with respect to the scale of the problem or structure and degree of uncertainty. For each problem, a set of different methods will be developed. The methods will improve decision support for the specific problems while simultaneously allowing a systematic analysis of dependencies between problem and methodology performance. To this end, additional measures will be developed to classify method performance, for example, decision speed, or the interpretability of a method. Based on the problem and method measures and the extensive experiments and analyses, a framework will be developed to guide future method design for this emerging research field.

View project in the research portal

The graduate school "Navigating the Chaos of Innovation and Transformation" (NACHOS) at Otto von Guericke University investigates how innovations can be successful from a technical, economic and social perspective. The aim is to research and link social, cultural and economic factors in the introduction of innovations. A particular focus is on the active involvement of employees, customers and society in the innovation process.
NACHOS is a joint project of the Faculty of Business Administration and Economics and the Faculty of Human Sciences and pursues an integrated approach. It uses perspectives and methods from the humanities and economics to specifically examine the social and cultural factors of innovation and their interaction with economic or technical aspects.
The guiding question is how an innovation can be technically, economically and socially successful and how these three dimensions relate to each other in order to ultimately improve the conditions for the success, adaptation and dissemination of innovations. Methodological approaches from the economic and human sciences are combined for this purpose.
This text was translated with DeepL on 28/11/2025

View project in the research portal

The sustainable use of renewable energies for power generation increasingly requires the integration of various energy infrastructures for the storage and use of energy. In view of varying investment costs, different lifetimes of technologies and volatile energy prices, financial evaluation plays a central role. In particular, the question arises as to when and to what extent cross-sector coupling is required. The SmartMES project focuses on connecting the electrical and thermal energy systems. In the sub-project of the Chair of Innovation and Financial Management, the focus is on the application of financial mathematical methods with the aim of evaluating the flexibility potential associated with such energy infrastructures - so-called real options - in a data-driven or simulation-based manner.
This text was translated with DeepL on 26/02/2026

View project in the research portal

Despite the high pace of innovation in the field of smart technologies and their crucial importance for sustainable social transformation processes in the areas of renewable energy, the environment and demographic change, many of these innovations face considerable uncertainty with regard to their success. They often fail as early as the launch phase - whether due to insufficient market knowledge, a lack of technology standards or a lack of consumer confidence in their direct benefits. The EU-funded interdisciplinary Graduate School Navigating the Chaos of Innovation and Transformation (NACHOS) aims to investigate, model and optimize the prerequisites for the success, dissemination and adaptation of smart innovations. The sub-project of the Chair of Innovation and Financial Management is dedicated to the analysis of how the decision-making behavior of social systems influences the evaluation of uncertain investment decisions and derives recommendations for the value-oriented control of entrepreneurial innovation processes.
This text was translated with DeepL on 26/02/2026

View project in the research portal

The ESF-funded graduate school “Navigating the Chaos of Innovation and Transformation” (NACHOS) at Otto von Guericke University investigates how innovations can be successful from a technical, economic, and social perspective. The aim is to research and connect social, cultural, and economic factors in the introduction of innovations. A particular focus is on the active involvement of employees, customers, and society in the innovation process. NACHOS is a joint project of the Faculty of Economics and the Faculty of Humanities and aims to take an integrated approach. It aims to use perspectives and methods from the humanities and economics to specifically investigate the social and cultural factors of innovations and their interaction with economic or technical aspects. The connecting question is how an innovation can be successful technically, economically, and socially, and how these three dimensions relate to each other in order to ultimately improve the conditions for the success, adaptation, and diffusion of innovations. Methodological approaches from economic and human sciences will be combined.

Scientific objectives of NACHOS:

Many innovations in mobility, energy, production, and care fail not because of technical feasibility, but because of the reaction of the people involved and society during introduction, implementation, and establishment. Previous approaches have focused either on understanding social impacts and needs or on economic process design. An integrated approach is currently lacking. The scientific goal of the project is to develop an integrated perspective to answer how an innovation can be successful both economically and socially.

The NACHOS work program:

The work program consists of eight interconnected subprojects (SP). Each subproject investigates a specific field of application related to the global goals of the graduate school. Two subprojects are primarily located in one of four dimensions: introduction, implementation, establishment, and culture & ethics. In each dimension, one subproject focuses on understanding needs and social impacts related to innovations, while the other focuses on designing economic processes. Both come together in scientific modeling, which abstracts and quantifies understanding and serves as input for design. Each subproject follows the established approach of the respective discipline.

View project in the research portal

In today's dynamic business environment, companies are increasingly pressured to stand out not just in terms of profit margins but also through innovative workplace strategies. Consequently, optimizing operational processes while accommodating diverse preferences of employees and customers has become increasingly crucial.

Recognizing and addressing employee preferences, such as flexible working hours and tasks tailored to their skill level and abilities, not only enhances job satisfaction and productivity but also fosters a more harmonious work environment. Similarly, considering customer preferences, such as service within desired time windows, can significantly enhance service quality and overall satisfaction levels. Meeting these objectives requires sophisticated planning and decision-support tools.

This project explores innovative solutions to identify and integrate diverse preferences into workforce and route planning. Specifically, we will investigate the influence of incorporating employee preferences spanning i.e., task types, work areas, and equitable workload distribution, ensuring optimal resource allocation. These approaches will be explored across various sectors, including last-mile delivery and the complex field of home health care routing and scheduling.

View project in the research portal

Scheduling is one of the most relevant planning tasks in operations management and describes the sequencing of jobs on machines. In practice, so-called hybrid flow shops (HFS) are often found, i.e., production lines in which several machines are available in the individual production stages. This means that, in addition to the sequence of the jobs, the jobs must also be assigned to the individual machines. Using lot streaming, i.e., the early transfer of sublots to subsequent production stages, can increase efficiency. However, quantifying the benefits of lot streaming is still necessary and tackled in this project. Because traditional planning approaches are usually unable to solve the complexity of this planning task, solution algorithms for this problem will be developed and evaluated. Problem properties are exploited to improve the approaches further. The developed algorithms can enable higher productivity and efficiency in industrial production in the future.

View project in the research portal

The doctoral project deals with dynamic scheduling in circular economies. The aim is to develop mathematical optimization models that enable efficiently reusing valuable resources from product returns at the end of the product life cycle while taking into account classic production goals such as cost minimization, short throughput times, and high delivery reliability. The research focuses on modeling the dynamic and stochastic nature of used product returns, caused by fluctuations in arrival times, quantities, and qualities.

The project contributes to the further development of sustainable production strategies in the spirit of a circular economy.

View project in the research portal

This project focuses on a type of Inventory Routing Problem, i.e., ATM cash replenishment problem. The problem involves balancing inventory levels in a way that avoids excessive inventory, which would incur daily interest on extra cash, and also avoids understocking, which would lead to stockouts and subsequent customer dissatisfaction. Since this process involves high-security vehicles transporting significant amounts of cash, routing costs must be factored in. This makes the objective function especially interesting to study, as maintaining inventory levels must be balanced with the need for not too many visits. Several real-world constraints are considered. One area of focus is on studying the stochastic nature of withdrawal demand at different ATMs. The resulting routes are examined to study the impact of multiple planning periods on the inventory levels using a rolling horizon approach. The aim of the study is to enable banks to optimally transport cash while better meeting customer demands.

View project in the research portal

This study examines a heuristic optimisation method for assigning and scheduling multi-skilled workers in a semi-automated electronics production environment pertaining to a Siemens production line. The study focuses on efficiently assigning orders and qualified workers to manufacturing cells that can process only specific product families, with the overall objective of minimising total production time. The problem incorporates several realistic features, including heterogeneous production cells, varying worker qualifications across processes, reduction coefficients that increase production intensity with more skilled workers, sequence-independent setup times between product family groups, and shift-based operations with limited worker availability. While a Mixed-Integer Linear Programming (MILP) model defines the exact problem formulation, solving large-scale instances requires a heuristic approach that constructs feasible assignments and then improves them through local search operators to obtain near-optimal schedules. This project contributes to the broader goals of Industry 5.0, promoting human–machine collaboration, workforce flexibility, and data-driven scheduling in advanced manufacturing systems.

View project in the research portal

The ExplAIn-TrAIn-Plan project addresses the increasing complexity of modern railway networks and the growing diversity of locomotive types. These present significant challenges for railway operators in achieving efficient and robust locomotive circulation planning. The project aims to develop AI-supported optimization and simulation methods to identify energy-efficient and robust planning variants and to assess their effects on vehicle circulation and crew schedules. By combining optimization, simulation, and machine learning, the project creates explainable and practically relevant decision support systems that enable sustainable and reliable planning. The international consortium brings together ÖBB-Produktion GmbH as an industry partner with its cooperate program ARP - Automated Resource Planning and interdisciplinary expertise in data analysis, optimization, simulation, and railway operations.

View project in the research portal

To compete with e-commerce giants such as Amazon, many local businesses start to offer fast same-day delivery, often within a few hours after an order was placed. Deliveries are conducted by local delivery fleets. However, the narrow delivery times and the geographical spread of pickup and delivery locations result in a lack of consolidation opportunities. This can be remedied by so-called micro hubs, which can serve as transhipment centres for parcels in urban delivery. Drivers can store parcels from adjacent shops for redistribution. They also can pick up parcels from different shops for joint delivery to customers in the same region. Thus, micro hubs can increase consolidation opportunities and may also enable the use of smaller, green, and clean vehicles for first and last mile delivery. Within this project, optimisation models incorporating consolidation centres in the pickup and delivery system of urban same day delivery are developed. Further, different solution approaches will be investigated to cope for the uncertainty in demand at time of planning.

View project in the research portal

In traditional scheduling research, machines are supposed to be only able to process one job at a time. However, in several real-world situations, machines can process several jobs in parallel up to a given capacity. One example is the growing of crops in greenhouses. The arising capacitated scheduling problems form a generalization of well-studied scheduling problems and have rarely been studied in the literature. We analyze the characteristics of these problems in various settings, such as flow shop or job shop. Tailored algorithms are developed to solve realistic problem instances considering multiple objectives.

View project in the research portal

Large manufacturing companies often manage a network of multiple factories, creating distributed scheduling problems. These problems involve assigning jobs to one of several distributed factories and sequencing the jobs within their designated factories. However, planning in distributed environments also requires the transport of jobs to factories. These transports are usually neglected in the existing planning approaches but can significantly impact the generated plans. We analyze the impact of transportation concerning classical scheduling objectives and environmental objectives such as emissions and energy consumption. Furthermore, inter-factory transports of intermediate goods are analyzed.

View project in the research portal

A pandemic can immobilize municipalities within a short amount of time. The key is to discover and avoid spreading of infection clusters through fast and effective testing. An innovative idea implemented during the COVID-19 pandemic in metropolitan areas such as Vienna, Austria, is the employment of a workforce of mobile testers. This project deals with the operational management of such mobile testers and the resulting impact on the spread of a disease using COVID-19 as an example.Based on state-of-the-art multi-agent simulation models, we will generate and analyze data on the tem-poral and spatial spreading (descriptive analytics). With methods of predictive analytics, we will aggregate the data to a detailed information model with a particular focus on modelling correlation for testing de-mand. Using this, we will model and solve the dynamic tester routing with infection hot spots and correla-tion demand problem (TRISC) using methods of prescriptive analytics, esp. reinforcement learning. The obtained policies will be evaluated by the multi-agent simulation again.Hypotheses / research questions / objectivesThe following core research questions will be investigated: (1) How can data of the spread of highly infec-tious diseases like COVID-19 be analyzed and modeled for the purpose of dynamic workforce control? (2) How can we achieve an effective dynamic control of the workforce in reaction and in anticipation of the complex disease information? (3) When is anticipatory dynamic workforce control effective in containing the spread of pandemics?The problem at hand shows new and severe complexity in the information model of the demand (test requests) and in the decision model for the operational control. Deriving the demand information model (via predictive analytics) is complex because it must capture the spatial-temporal correlation of demand. The decision model for the problem is a novel stochastic and dynamic vehicle routing problem. Determin-ing high-quality decisions that integrate the information model (via prescriptive analytics) is therefore additionally challenging. The evaluation by an established agent-based simulation is particularly excep-tional for this research field.The project will be conducted by Jan Fabian Ehmke (JE, Universität Wien), Marlin Ulmer (MU, Technische Universität Braunschweig), and Niki Popper (NP, Technische Universität Wien). JE will serve as coordina-tor and is responsible for tasks of predictive analytics. MU leads the project part on prescriptive analytics for dynamic vehicle routing. NP will contribute with an agent-based simulation that supports the creation of the predictive information model and the evaluation of dynamic and stochastic disease sampling. This will provide unique opportunities to extend current methods including their evaluation in the urgent ap-plication of disease routing.

View project in the research portal

The novel right-to-repair legislation intends to extend the usage period of consumer durables by enabling customers to repair their products when becoming defective. This right-to-repair requires manufacturers of new product to changes the product design to make it repairable (design for repair), but they also must supply the customers with reasonably prices spare parts. Remanufacturers also might profit from easier access to spare parts. Therefore, the questions arise, how the new legislation impacts manufacturers, consumers, and remanufacturers, and what impact it will have on the environment. To answer these questions, we are use strategic decision models and game theoretic solution approaches.

View project in the research portal

We analyze planning and operations in restaurant meal-delivery, We consider the design of different delivery systems. We further optimize demand and fleet control in an integrated manner, and use machine learning for delivery time predictions.

View project in the research portal

Local delivery platforms are collaborative undertakings where local stores offer instant-delivery to local customers ordering their products online. Offering such delivery services both reliably and cost-effectively is one of the main challenges for local delivery platforms as they face a complex, stochastic, dynamic pickup-and-delivery problem. Orders need to be consolidated to increase the efficiency of the delivery operations and thereby enable a high service guarantee towards the customer and stores. But, waiting for consolidation opportunities may jeopardize delivery service reliability in the future, and thus requires anticipating future demand. This project introduces a generic approach to balance the consolidation potential and delivery urgency of orders. Inspired by a motivating application in the city of Groningen, the Netherlands, numerical experiments show that this approach strongly increases perceived customer satisfaction while lowering the total travel time of the vehicles compared to various benchmark policies. It also reduces the percentage of late deliveries, and the extent of their lateness, to a minimum.

View project in the research portal

Peer-to-peer transportation platforms dynamically match requests (e.g., a ride, a delivery) to independent suppliers who are not employed nor controlled by the platform. Thus, the platform cannot be certain that a supplier will accept an offered request. To mitigate this selection uncertainty, a platform can offer each supplier a menu of requests to choose from. However, such menus need to be created carefully because there is a trade-off between selection probability and duplicate selections. In addition to a complex decision space, supplier selection decisions are vast and have systematic implications, impacting the platform’s revenue, other suppliers’ experiences (in the form of duplicate selections) and the request waiting times. Thus, we present a stochastic optimization. Extensive computational results using the Chicago Region as a case study illustrate that our method outperforms a set of benchmark policies. Our method leads to more balanced assignments by sacrificing some easy wins towards better system performance over time and for all stakeholders involved, including increased revenue for the platform, and decreased match waiting times for suppliers and requests.

View project in the research portal

Existing powerful operations research methods enable the creation of highly efficient plans for deploying personnel and vehicles in rail transport. In the implementation, however, delays and breakdowns mean that the plans can often not be executed as intended. To meet this challenge, in the project VIPES, methods are developed to design schedules for traction units and shift schedules for train crews in such a way that they are efficient and reliable at the same time. This is to be made possible by intelligent interaction between optimization and simulation. Machine learning techniques are used to identify efficient and reliable solution structures, which will be used in the solution procedures.

View project in the research portal

The joint project deals with the question of how suitable charging facilities can be created in existing housing stock, especially buildings with several residential or usage parties, as part of the energy and mobility transition.
The aim here is to create cost-effective, convenient, comprehensible and easy-to-use charging facilities for electric vehicles in buildings with several tenants. A technical and organizational solution, in the form of an energy management system, is to be proposed at the building and neighbourhood level, which particularly addresses the tenants of buildings with several parties. As part of the energy management system, a charging management system coordinates the charging processes according to the tenants' needs and taking into account decentralized power generation systems (photovoltaic system and combined heat and power plant).
Within the sub-project "Integrated approach to incentive-compatible optimization of charging management", concrete tariff and compensation models are to be developed in order to create incentives for maintaining a demand calendar. The objectives of this sub-project include the (further) development of a mathematical optimization model that underlies charging management and the investigation of the effect of different tariff and incentive models in order to derive recommendations for use in apartment buildings.
This text was translated with DeepL on 26/02/2026

View project in the research portal

The goal of this project is to provide effective decision support for stochastic dynamic pickup and delivery problems by combining the strengths of mixed-integer linear programming (MILP) and reinforcement learning (RL).Stochastic dynamic pickup-and-delivery problems play an increasingly important role in urban logistics. They are characterized by the often time-critical transport of wares or passengers in the city. Common examples are same-day delivery, ridesharing, and restaurant meal delivery. The mentioned problems have in common that a sequence of decision problems with future uncertainty must be solved in every decision step where the full value of a decision reveals only later in the service horizon. Searching the combinatorial decision space of the subproblems for efficient and feasible tours is a complex task of solving a MILP. This complexity is now multiplied by the challenge of evaluating such decision with respect to their effectiveness given future dynamism and uncertainty; an ideal case for RL. Both are crucial to fully meet operational requirements. Therefore, a direct combination of both methods is needed. Yet, a seamless integration has not been established due to different reasons and is the aim of this research project. We suggest using RL to manipulate the MILP itself to derive not only efficient but also effective decisions. This manipulation may change the objective function or the constraints. Incentive or penalty terms can be added to the objective function to enforce or prohibit the selection of certain decisions. Alternatively, the constraints may be adapted to reserve fleet-resources.The challenge is to decide where and how the manipulation takes place. SDPDPs have constraints with respect to routing, vehicle capacities, or time windows. Some constraints may be irrelevant for the fleet’s flexibility while others might be binding. The first part of the research project focuses on identifying the "interesting” parts of the MILP via (un-)supervised learning. Once the "interesting” parts are identified, the second challenge is to find the right parametrization. Here, we will apply RL methods to learn the state-dependent manipulation of the MILP components.

View project in the research portal

There has been a paradigm-shift in urban logistic services in the last years; global interconnectedness, urbanization, ubiquitous information streams, and increased service-orientation raise the need for anticipatory real-time decision making. A striking example are logistic service providers: Service promises, like same-day or restaurant meal delivery, dial-a-ride, and emergency repair, force logistic service providers to anticipate future demand, adjust to real-time traffic information, or even incorporate unknown crowdsourced drivers to fulfill customer expectations. Data-driven, anticipatory approaches are required to overcome the challenges of such services. They promise to improve customer satisfaction through accurate predictions (e.g., via supervised learning), enhanced fleet control (e.g., via reinforcement learning), and identification of demand patterns and delivery scenarios (e.g., via unsupervised learning). Within this research project, we combine recent advances in machine learning with established methods from operations research to tackle present-day challenges in urban logistics.

View project in the research portal

This project develops a case study detailing the reaction of an alternative food pantry to the Coronavirus. The alternative food pantry provided produce, dairy, meat, and cereals to around 150 families each week before the virus. Due to social distancing and concerns about spreading infection, the food distribution process needed to be quickly modified. This paper examines their procurement, transportation, and distribution operations before and during the virus crisis. This juxtaposition highlights the changes that the unfolding pandemic necessitated and the various ways food pantries can organize their distribution.

View project in the research portal

From the perspective of a firm providing on-location services, we address the problem of determining service time windows that must be communicated to customers at the time of request. We set service time windows under incomplete information on arrival times to customers. We show how to minimize expected time window width subject to a constraint on service level. We use analytical results of the problem to inspire a practice-ready heuristic for the more general case. Relative to the industry standard of communicating uniform time windows to all customers, and to other policies applied in practice, our method of quoting customer-specific time windows yields a substantial increase in customer convenience without sacrificing reliability of service.

View project in the research portal

In this project, we study the problem of offering fair same-day delivery (SDD)-service to customers. The service area is partitioned into different regions. Over the course of a day, customers request for SDD service, and the timing of requests and delivery locations are not known in advance. The dispatcher dynamically assigns vehicles to make deliveries to accepted customers before their delivery deadline. In addition to overall service rate, we maximize the minimal regional service rate across all regions by means of reinforcement learning. Computational results demonstrate the effectiveness of our approach in alleviating unfairness both spatially and temporally in different customer geographies. We also show this effectiveness is valid with different depot locations, providing businesses with opportunity to achieve better fairness from any location. Further, we consider the impact of ignoring fairness in service

View project in the research portal

We consider an urban instant delivery environment, e.g., meal delivery, in which customers place orders over the course of a day and are promised delivery within a short period of time after an order is placed. Deliveries are made using a fleet of vehicles, each completing one or more trips during the day. To avoid missing delivery time promises as much as possible, the provider manages demand by dynamically adjusting the size of the service area, i.e., the area in which orders can be delivered. The provider seeks to maximize the number of orders served while avoiding missed delivery time promises. We analyze several techniques to support the dynamic adjusting of the size of the service area which can be embedded in planning and execution tools that help the provider achieve its goal. Extensive computational experiments demonstrate the efficacy of the techniques and show that dynamic sizing of the service area can increase the number of orders served significantly without increasing the number of missed delivery time promises.

View project in the research portal

Eco-labels are a way to benchmark transportation shipments with respect to their environmental impact. In contrast to an eco-labeling of consumer products, emissions in transportation depend on several operational factors like the mode of transportation (e.g., train or truck) or a vehicle’s current and potential future capacity utilization when new orders are added for consolidation. Thus, satisfying eco-labels and doing this cost-efficiently is a challenging task when dynamically routing orders in an intermodal network. In this project, we analyze how reinforcement learning techniques can be adapted to our problem and show their advantages and the impact of Eco-labels in a comprehensive study for intermodal transport via train and trucks in Europe.

View project in the research portal

The introduction of proprietary parts to gain a competitive advantage over independent remanufacturers is a strategy often used by OEMs. In this research project, we use a strategic modeling approach to consider an OEM competing with an independent remanufacturer (IR) that sells the OEM's remanufactured products that compete with the new products. The OEM is considering the use of proprietary parts in order to exert a stronger influence on the secondary market. The aim of the study is to better understand the influence of the product design decision on the price competition between OEM and IR.
This text was translated with DeepL on 26/02/2026

View project in the research portal

The question of the project deals with investment in electricity generation and storage technologies. This question arises in particular for owners of single-family homes and multi-family homes as well as small and medium-sized SMEs, as an investment represents a relatively large long-term financial risk. In addition, it is becoming increasingly difficult to select a suitable technology in which to invest.

The aim of the project is to develop a methodology for complex investment decisions under uncertainty as well as under the aspect of self-consumption coverage or energy marketing. The aim is to find a system solution that is optimal in practice. This system solution must be identified based on a large technology pool for generation, storage and conversion and at the same time fulfill the critical aspects of cost-effectiveness, efficiency, environmental compatibility and safety. In addition, this optimization should be considered for time steps below 1/4 h.

With these results, grid operators can advance the development of a methodology for the improved prediction of changing consumption profiles of prosumers & SMEs. In addition, recommendations for action can be made with regard to various aspects of balancing group management.

This project is supported by the state of Saxony-Anhalt with funds from the European Regional Development Fund (ERDF).
This text was translated with DeepL on 26/02/2026

View project in the research portal

In times of globalization and increasing international networking and an associated more uncertain, more complex and dynamic world, the need for modern valuation approaches in financial management is increasing. Uncertainty per se can have a variety of causes and arise, for example, from different technologies, the macroeconomic, political and regulatory environment. Dealing with it poses a particular problem in practice. For example, the valuation of research and development investments or (energy) infrastructure projects regularly fails.

The aim of this research project is to map various sources of uncertainty using model theory in order to (further) develop models that are as holistic as possible for the assessment of financial problems. Examples of this include the influence of uncertainty on construction time, competitive behavior, technical failure probabilities and learning behavior.
This text was translated with DeepL on 26/02/2026

View project in the research portal

E-Commerce has increased sales by two-digit percentages in the last years. In the future, same-day delivery (SDD) will become a major success factor for E-Commerce companies. However, offering SDD is expensive because short delivery deadlines and subsequently ordering customers leave little room for consolidation. To cost-efficiently provide SDD, decision support methods are required. On the operational level, these methods dynamically create, update, and adapt delivery tours based on newly revealed information. For effective decision making, these methods need to anticipate both the detailed short term impact as well as the general long-term impact of a decision. SDD-problems form a subgroup of stochastic dynamic vehicle routing problems. This problem class is relatively new and general methods are not established yet. Because of the high complexity of dynamic vehicle routing problems, exact methods cannot be applied. First work in this area draws on heuristic methods of approximate dynamic programming (ADP). ADP-methods use simulation of the dynamic model to approximate a decision’s impact on the future. These methods can be differentiated based on the time these simulations take place. Online methods start simulating in the actual decision state. Offline methods conduct simulations before the decision process starts. They store the aggregated results and access them during the actual decision process. Online methods can simulate using full detail of a decision state but only with limited calculation time available. Offline methods allow frequent simulations and reliable long-term approximations, however, on an aggregated level. For the SDD-problem at hand, both short-term detail and long-term reliability are essential for successful decision support. However, both online and offline methods fall short in one of the two capacities. A combination is necessary. This research projects aims on developing a combined ADP-method for the SDD-problem. The method allows a generic, state-dependent shift between online and offline simulation results. The method will provide effective decision support and business insight for a new and important SDD-problem. Further, this method will be generic and broadly applicable in the field of dynamic vehicle routing. It will therefore be an important step towards a general solution framework in dynamic vehicle routing.

View project in the research portal

As part of the energy transition, Germany's energy supply is to be gradually converted to renewable energies. Saxony-Anhalt is playing a pioneering role in this regard. With its high share of renewable energies in gross electricity generation, it is a positive example of a successful state funding policy compared to the rest of Germany. Various energy planning tools are used to implement the latter, but they rarely take into account a large number of secondary conditions (technical, economic, regulatory, political). Moreover, they do not adequately reflect the complex reality. In particular, the optimal investment decision (under uncertainty) and the optimal schedule for the plant pool selected from generation, storage and conversion technologies are highly complex decisions that cannot be easily made by the stakeholders. Due to the complexity of the decision, significant methodological challenges arise in the implementation of investment decisions on the prosumer side and in the corresponding optimization of self-consumption coverage, which are to be addressed in the research project.
This text was translated with DeepL on 26/02/2026

View project in the research portal

How to best deliver goods to consumers has been a logistics question since time immemorial. However, almost all traditional delivery models involved a form of company employees, whether employees of the company manufacturing the goods or whether employees of the company transporting the goods. With the growth of the gig economy, however, a new model not involving company employees has emerged: relying on crowdsourced delivery. Crowdsourced delivery involves enlisting individuals to deliver goods and interacting with these individuals using the internet. In crowdsourced delivery, the interaction with the individuals typically occurs through a platform. Importantly, the crowdsourced couriers are not employed by the platform, and this has fundamentally changed the planning and execution of the delivery of goods: the delivery capacity is no longer under (full) control of the company managing the delivery. We analyze the challenges this introduces, review how the research community has proposed to handle some of these challenges, and elaborate on the challenges that have not yet been addressed.

View project in the research portal

In addition to economic goals, social and ecological interests are increasingly coming to the fore in society. These are ratified, for example, in the 17 Sustainable Development Goals of the United Nations (UN). Profit-oriented companies are therefore also increasingly confronted with the need to take into account the social and ecological interests of their stakeholders. Examples include the demand for sustainable products by customers, stricter ecological requirements by legislators or the desire for sustainable projects by shareholders. Companies are therefore increasingly investing in sustainability projects. When planning, evaluating and implementing these projects, these political and social factors and uncertainties must be taken into account in addition to the economic uncertainty. The aim of this research project is to identify the particular factors influencing a company's potential sustainability projects and to describe them as an investment opportunity under uncertainty. In particular, the critical stakeholders are to be identified and the value and value drivers of the investment are to be determined to support decision-making. Among other things, game theory and principal-agent problems are also taken into account.
This text was translated with DeepL on 26/02/2026

View project in the research portal

The FinTech Financial Technology is an ego. incubator for the development and testing of innovative concepts and solutions. Advancing digitalization and the increasing acceptance of cryptocurrencies, especially blockchain technology, will have a significant impact on the real economy and the banking sector in the future. The expected disruptive changes will give rise to new innovative products and services. The aim of the ego. incubator is to support students and academic staff interested in founding a company in the development of new product and service ideas in the field of financial technologies. To this end, the FinTech is equipped with state-of-the-art hardware and software applications, such as the Ethereum blockchain and a high-performance computer for deep learning and AI applications.

This project is supported by the state of Saxony-Anhalt with funds from the European Regional Development Fund (ERDF).
This text was translated with DeepL on 26/02/2026

View project in the research portal

Corporate finance is changing dramatically as a result of digitalization. Cryptocurrencies are replacing fiat money, blockchain technologies are taking over the tasks of global accounting and smart contracts are controlling the financial supply chain of companies. The research project aims to develop a proof-of-concept for various financial problems based on selected digital technologies (Ethereum, Corda, HyperLedger) and to examine their practical suitability. Current information is available at: www.fintech.ovgu.de.
This text was translated with DeepL on 26/02/2026

View project in the research portal

Personnel and vehicle costs are the main cost components in the operation of rail transport networks in both passenger and freight transport. The efficient use of these costs is therefore of central importance for rail transport companies. At the same time, the relevant planning problems are very complex and require powerful operations research methods to solve practical instances. The project deals with the development of these methods, taking into account practical requirements. In particular, the shift planning of train attendants and the locomotive assignment problem in freight transportation are considered.
This text was translated with DeepL on 26/02/2026

View project in the research portal

Machine scheduling is a classic task in production planning and control. Metaheuristics are usually used in current research. In this research project, extensions of the hybrid flowshop problem are considered in particular. On the one hand, the consideration of energy consumption is analyzed, which leads to multi-criteria optimization problems. Another aspect is the possibility of dividing orders into sub-orders. The influence of this so-called lot streaming on completion times and cycle times is being investigated as part of this project.
This text was translated with DeepL on 26/02/2026

View project in the research portal

In particular, the difficulty of predicting consumer behavior is making it increasingly difficult to evaluate innovation projects and new technologies from a financial perspective. Impressive evidence of this is currently the weak acceptance of e-cars or cashless payment transactions. A major advantage of this model is that the cash flow profile results from the behavior of the individual agents and does not have to be specified exogenously, as in neoclassical models. The aim of the research project is to develop an agent-based entrepreneurial decision model that takes into account the effect of social media and the influence of substitution technologies on purchasing decision behavior.
This text was translated with DeepL on 26/02/2026

View project in the research portal

Many investment decisions have to be made by several parties with different and often competing interests. These investment decisions cannot be fully captured and modeled without game theory methods. Examples include the implementation of a company acquisition, the establishment and scheduling of a joint venture or the expansion of capacity in a supply chain. At the same time, investment decisions are always decisions made under uncertainty, as the amount of future cash flows generated by an investment project is not yet known at the time of investment. The central statement of real option theory is that the possibility of waiting with the investment has a value in such an uncertain situation if the investment opportunity still exists later and more information about the cash flows generated by the investment becomes available in the meantime. However, the flexibility value of this waiting option must be given up at the time of investment. When determining the time of investment and the value of an investment opportunity, real option theory methods should therefore also be used. The aim of the research project is to combine game theory and real option theory in the modeling of investment decisions and thus generate new insights into investment timing and the distribution of the generated added value between the individual decision-makers. Of particular interest are the influence of uncertainty and the choice of game theory model.
This text was translated with DeepL on 26/02/2026

View project in the research portal

In a large number of publications on strategic decision problems in closed-loop supply chains, it is assumed when modeling the decision situation that consumers' willingness to pay for a remanufactured product represents a constant proportion (across all consumers) of their willingness to pay for the corresponding new product. This simplifying assumption makes it easier to derive structural statements. However, recent empirical studies question this assumption by showing that the discounting factors vary considerably among consumers. This project compares the solution under constant discounting factors with the solution that assumes a probability distribution for the discounting factors for different complex models.
This text was translated with DeepL on 26/02/2026

View project in the research portal

Innovations are an important economic success factor and a driving force for change in a company and society. In addition to process and social innovations, novel products and, in particular, their development, marketing and financing are of great importance. Both research and practice have shown that their phases can be characterized by means of the product life cycle or the product demand cycle. However, the exact development and thus the cash flows generated cannot be precisely predicted at the start of a project. The possibility of product innovation can therefore be understood as an investment decision under uncertainty. The central research interest of the project is to combine diffusion research and the company's flexibility of action when modeling investment decisions. Both the timing and the value of the investment opportunity are to be investigated as a function of decisive factors such as uncertainty and the characteristics of the product life cycle.
This text was translated with DeepL on 26/02/2026

View project in the research portal

Recent research has shown that consumers have significant concerns about the quality of remanufactured products, resulting in a lower willingness to pay for remanufactured products than for new products. To better understand this phenomenon, this project combines surveys and experimental studies to identify the causes of perceived lower quality and its influence on consumers' willingness to pay for remanufactured appliances. A better understanding of these relationships can be used for the planning of remanufacturing activities and the pricing of remanufactured products.
This text was translated with DeepL on 26/02/2026

View project in the research portal

One procurement strategy frequently used by companies is the combined use of capacity reservation contracts and the spot market. This is intended to achieve a balance between the respective risks associated with the individual procurement sources. This project analyzes both optimal and simplified strategies for long-term capacity reservations and periodic order/stock decisions using the above sources under stochastic demand and random spot market price fluctuations. The aim is to find structural properties of the optimal combined purchasing policy under different conditions. Using these properties, heuristics will be developed.
This text was translated with DeepL on 26/02/2026

View project in the research portal

It is a company's strategy that allows it to operate competitively on the market. Especially in times of globalization and technological progress, the market environment for companies is constantly changing. In addition, there is a strong asymmetry between small local companies and global players. These new aspects require an updated strategy concept that provides corporate strategies for both local and global players. The aim of the research project is to identify corporate strategies for both local and global companies that will enable them to survive sustainably in globalized markets. These corporate strategies should be applicable in theory as well as in practice and their formulation should be accessible in such a way that managers and other decision-makers can be provided with easy-to-use instructions for action. In addition, the accessibility of the corporate strategies to be formulated is also important in order to enable the company management to provide employees with uniform guidelines for a mission or corporate strategy.
This text was translated with DeepL on 26/02/2026

View project in the research portal

Many mergers & acquisitions (M&A) fail and cause enormous welfare losses (e. g. plant closings, lay-offs, tax losses). Reasons can be an inappropriate purchase price, a failed target integration, volatile economic environments and times. To cope with these problems risk reducing techniques like earn-outs or partial acquisitions become increasingly popular. The success of these M&A transactions and their relevant parameters can be analyzed by different and complementary approaches. All these have in common that they are based on corporates’ stock market reactions caused by these transaction events. The most appropriate method to investigate the short-term success of M&A is to conduct an event study. In this case the stock market returns for companies that announce an earn-out are adjusted by the normal reaction of a stock market index. The long-term success of earn-outs can be investigated with the help of the Fama-French Model, buy-and-hold returns or stochastic dominance. The simplest method is the latter one to show long-term success by comparing the cumulative density function between event companies and non-event companies. The primary goal is the identification of relevant success factors to gain a better understanding for risk reducing techniques in acquiring companies. The secondary goal is to find theoretical support of economic theories (transaction costs, agency and real options theory) for earn-outs.

View project in the research portal

Das Bestandsmanagement von Ersatzteilen stellt Hersteller langlebiger Industrie- und Konsumgüter insbesondere in der Nachserienphase vor große Herausforderungen, da nach Abschluss der Serienproduktion die zu einer effizienten Bedarfsbefriedigung notwendige Flexibilität stark eingeschränkt ist. Ziel des Projektes ist die Entwicklung eines Entscheidungsunterstützungssystems für komplexe Beschaffungsstrategi­en, bestehend aus Kombinationen der am häufigsten genutzten Beschaffungsoptionen in Form von Abschlusslosbildung zu Serienbedingungen, Nachproduktion in kleineren Losen und Aufarbeitung von Altprodukten.

View project in the research portal

In this project, order-oriented production in the mechanical workshop production of small and medium-sized enterprises (SMEs) is considered. The main planning tasks are machine allocation and personnel deployment planning with employee qualification as the connecting element. In the literature and in practical application, the sub-problems are largely considered separately and often solved successively. However, such an approach is particularly problematic in SMEs, as interdependencies between the sub-problems cannot be sufficiently taken into account. This is the case, for example, when qualified personnel have to accompany the processes on the machines throughout, but are only available to a limited extent. The aim is to develop efficient models and solutions for the simultaneous planning of machine occupancy and personnel deployment.
This text was translated with DeepL on 26/02/2026

View project in the research portal

Herrstellern langlebiger Wirtschaftsgüter verpflichten sich häufig zur langfristigen Bereitstellung von Ersatzteilen. Die effiziente Bereitstellung dieser Ersatzteile stellt demzufolge eine Hauptaufgabe im After-Sales-Geschäft dar. Neben traditionellen Beschaffungsoptionen wie einem Endbevorratungslos und der Aufarbeitung von Altteilen, die in ihrer Flexibilität jeweils großen Einschränkungen unterliegen, stellt der Rückkauf von defekten Produkten eine nützliche weitere Option dar. Obwohl dieser mit hohen direkten Ausgaben verbunden sein kann, lassen sich aufgrund der nun nicht mehr notwendigen Bedarfsbefriedigung zusätzliche Kostensenkungspotentiale erschließen. Neben einer größeren Kontrolle über den Bedarf an Ersatzteilen erhöht der Rückkauf auch die Verfügbarkeit an aufzuarbeitenden Altprodukten, wodurch sich letztlich die auf lange Sicht sehr teure Endbevorratung mit dem Abschlusslos reduziert lässt. Im Rahmen dieses Projektes werden mit quantitativen Methoden optimale Rückkaufstrategien unter verschiedenen Rahmenbedingungen bezüglich der Verfügbarkeit an Informationen und Flexibilität der Rückkaufoption untersucht, aus denen Handlungsempfehlungen für den Einsatz in der Praxis abgeleitet werden.

View project in the research portal

Im Rahmen immer kürzer werdender Produktlebenszyklen stellt das Bestandsmanagement von Ersatzteilen eine zunehmend schwierige Aufgabe für die Hersteller von Originalteilen dar. Diese versuchen nun, dieser Herausforderung durch die Schaffung neuer Optionen für die Befriedigung der Bedarfe zu begegnen. Insbesondere die Aufarbeitung von Altprodukten stellt hier eine lohnenswerte Alternative zur Neuproduktion dar. Dem steht jedoch die teilweise nur mangelhafte Verfügbarkeit von Altprodukten entgegen, da sich auch andere um den lukrativen Ersatzteilmarkt konkurrierende Unternehmen dieser Werte bewusst sind. Anhand eines Fallbeispiels soll untersucht werden, ob sich beispielsweise mit Rücknahmepreisen sowohl die Rücknahme von Altprodukten als auch der Marktanteil des Herstellers von Originalteilen steigern lassen.

View project in the research portal