CHAIR OF

MANAGEMENT

SCIENCE

Trotz der hohen Innovationsdynamik im Bereich der Smart-Technologies und ihrer entscheidenden Bedeutung für nachhaltige gesellschaftliche Transformationsprozesse in den Feldern regenerative Energie, Umwelt und demographischer Wandel, stehen viele dieser Innovationen vor erheblichen Unsicherheiten hinsichtlich ihres Erfolgs. Häufig scheitern sie bereits in der Einführungsphase – sei es durch unzureichende Marktkenntnisse, fehlende Technologiestandards oder mangelndes Vertrauen der Konsumenten in ihren direkten Nutzen. Die durch die EU geförderte interdisziplinäre Graduate School Navigating the Chaos of Innovation and Transformation (NACHOS) verfolgt das Ziel, die Voraussetzungen für das Gelingen, die Verbreitung und die Anpassung smarter Innovationen zu untersuchen, zu modellieren und zu optimieren. Das Teilprojekt des Lehrstuhls für Innovations- und Finanzmanagement widmet sich dabei der Analyse, wie das Entscheidungsverhalten sozialer Systeme die Bewertung von unsicherheitsbehafteten Investitionsentscheidungen beeinflusst und leitet Handlungsempfehlungen zur wertorientierten Steuerungen von unternehmerischen Innovationsprozessen ab.

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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.

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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.This project will span six years and will be hosted at the TU München (TUM). During the project, the PI will supervise three PhD-students, each student working four years in one application area. The PI and the students will collaborate with researchers from TUM and the Georgia Institute of Technology.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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