SmartMobility: Innovative Mobilitäts- und Logistikkonzepte

Die Bedeutung von Mobilität und Logistik hat in den vergangenen Jahren stetig zugenommen. Heute steht dieser Bereich für einen bedeutenden Anteil der Wirtschaftsleistung, aber auch des Ressourcenverbrauchs. Um die ehrgeizigen Umwelt- und Klimaschutzziele zu erreichen, besteht hier ein hoher Bedarf an innovativen Konzepten.

Shared Mobility

In diesem Bereich konzentrieren wir uns auf Bike- und Car-Sharing. Diese bieten, insbesondere im Zusammenspiel mit dem öffentlichen Personennah- und -fernverkehr, erhebliches Potential zur Einsparung fossiler Energieträger. So sind in den vergangenen Jahren weltweit in vielen Städten öffentliche Fahrradverleihsysteme entstanden. Bei diesen können Fahrräder an einer Vielzahl von Stationen automatisch entliehen und zurückgegeben werden. Moderne Car-Sharing Anbieter ermöglichen eine Ausleihe und Rückgabe auf nahezu beliebigen Parkplätzen innerhalb eines definierten Geschäftsgebietes (Free-Floating). Dabei konzentrieren wir uns auf folgende Fragen der strategischen Planung und Dimensionierung der Systeme sowie zur ihres operativen Betriebs und arbeiten mit einem namhaften deutschen free-floating Car-Sharing Anbieter zusammen.

  • Gestaltung von Shared-Mobility Systemen: Hier ergeben sich zahlreiche interessante Fragestellungen, die aktuell verstärkt in das Blickfeld der Forschung kommen. Dazu zählt etwa die Positionierung und Dimensionierung der Stationen.
  • Operative Steuerung von Shared-Mobility/E-Mobility Systemen: Nahezu alle neueren öffentlichen Verleihsysteme ermöglichen Einwegmieten, d.h. das Fahrzeug muss nicht am Ausleihort zurückgegeben werden. Bei asymmetrischer Nachfrage erfordert dies jedoch regelmäßig einen anbieterseitigen Rücktransport der Fahrzeuge, welcher einen erheblichen Teil der laufenden Betriebskosten verursacht. Daher zielt unsere Forschung auf die kostenminimale Gestaltung der Transporte bei Aufrechterhaltung einer hohen Verfügbarkeit.
  • Bepreisung von Shared-Mobility/E-Mobility Systemen:  Verglichen etwa mit dem Mobilfunkmarkt ist die Tarifstruktur von Shared-Mobility Anbietern bisher kaum entwickelt. Effektives Pricing ist jedoch nicht nur der Schlüssel zur Profitabilität, sondern kann auch zur operativen Steuerung der Nachfrage eingesetzt werden, wie bspw. Fluggesellschaften seit langem zeigen. In Shared-Mobility Systemen kann so die Verfügbarkeit der Fahrzeuge erhöht und kostspielige Rücktransporte durch den Anbieter reduziert werden.


  • Müller, C.; Gönsch, J.; Soppert, M.; Steinhardt, C.: Customer-Centric Dynamic Pricing for Free-Floating Shared Mobility Systems. 2021. VolltextBIB DownloadDetails

    Free-floating shared mobility systems offer customers the flexibility to pick up and drop off vehicles at any location within the business area and, thus, have become the most popular type of shared mobility system. However, this flexibility has the drawback that vehicles tend to accumulate at locations with low demand. To counter these imbalances, pricing has proven to be an effective and cost-efficient means. The fact that customers use mobile applications, combined with the fact that providers know the exact location of each vehicle in real-time, provides new opportunities for dynamic pricing.

    In this context, we develop a pricing approach for the dynamic online problem of a provider who determines profit-maximizing prices whenever a customer opens the provider’s mobile application to rent a vehicle. Our pricing approach has three distinguishing features: First, it is customer-centric, i.e., it considers the customer’s location as well as disaggregated choice behavior to precisely capture the effect of price and walking distance to the available vehicles on the customer’s propensity to choose a vehicle. Second, our pricing approach is origin-based, i.e., prices are differentiated by location and time of rental start, which reflects the real-world situation where the rental destination is usually unknown. Third, our approach is anticipative and uses a stochastic dynamic program to anticipate the effect of current decisions on future vehicle locations, rentals, and profits. As solution method, we propose a non-parametric value function approximation, which offers several advantages for the application, e.g., historical data can readily be used and main parameters can be pre-computed such that the online pricing problem becomes tractable. Extensive numerical studies, including a case study based on Share Now data, demonstrate that our approach increases profits by up to 13% compared to existing approaches from the literature and other benchmarks.

  • Soppert, M.; Steinhardt, C.; Müller, C.; Gönsch, J.; Bhogale, P.: Matching Functions for Free-Floating Shared Mobility System Optimization to Capture Maximum Walking Distances. In: European Journal of Operational Research, Jg. 305 (2023) Nr. 3, S. 1194-1214. doi:10.1016/j.ejor.2022.06.058VolltextBIB DownloadDetails

    Shared mobility systems have become a frequently used inner-city mobility option. In particular, free-floating shared mobility systems are experiencing strong growth compared to station-based systems. For both, many approaches have been proposed to optimize operations, e.g., through pricing and vehicle relocation. To date, however, optimization models for free-floating shared mobility systems have simply adopted key assumptions from station-based models. This refers, in particular, to the models’ part that formalizes how rentals realize depending on available vehicles and arriving customers, i.e., how supply and demand match. However, this adoption results in simplifications that do not adequately account for the unique characteristics of free-floating systems, leading to overestimated rentals, suboptimal decisions, and lost profits.

    In this paper, we address the issue of accurate optimization model formulation for free-floating systems. Thereby, we build on the state-of-the-art concept of considering a spatial discretization of the operating area into zones. We formally derive two novel analytical matching functions specifically suited for free-floating system optimization, incorporating additional parameters besides supply and demand, such as customers’ maximum walking distance and zone sizes. We investigate their properties, like their linearizability and integrability into existing optimization models. Our computational study shows that the two functions’ accuracy can be up to 20 times higher than the existing approach. In addition, in a pricing case study based on data of Share Now, Europe’s largest free-floating car sharing provider, we demonstrate that more profitable pricing decisions are made. Most importantly, our work enables the adaptation of station-based optimization models to free-floating systems.

  • Soppert, M.; Steinhardt, C.; Müller, C.; Gönsch, J.: Differentiated Pricing of Shared Mobility Systems Considering Network Effects. In: Transportation Science, Jg. 56 (2022) Nr. 5, S. 1111-1408. doi:10.1287/trsc.2022.1131VolltextBIB DownloadDetails

    Over the last decades, shared mobility systems have become an integral part of inner-city mobility. Modern systems allow one-way rentals, i.e. customers can drop off the vehicle at a different location to where they began their trip. A prominent example is car sharing. Indeed, this work was motivated by the insight we gained in collaborating closely with Europe's largest car sharing provider, Share Now. In car sharing, as well as in shared mobility systems in general, pricing optimization has turned out to be a promising means of increasing profit while challenged by limited vehicle supply and asymmetric demand across time and space. Thus, in practice, providers increasingly use minute pricing that is differentiated according to where a rental originates, i.e., considering its location and the time of day. In research, however, such approaches have not been considered yet. In this paper, we therefore introduce the corresponding origin-based differentiated, profit-maximizing pricing problem for shared mobility systems. The problem is to determine spatially and temporally differentiated minute prices, taking network effects on the supply side as well as several practice relevant aspects into account. Based on a deterministic network flow model, we formulate the problem as a mixed-integer linear program and prove that it is NP-hard. For its solution, we propose a temporal decomposition approach based on approximate dynamic programming. The approach integrates a value function approximation to incorporate future profits and account for network effects. Extensive computational experiments demonstrate the benefits of capturing such effects in pricing generally, as well as showing our value function approximation's ability to anticipate them precisely. Further, in a case study based on Share Now data from Florence in Italy, we observe profit increases of around 9% compared to constant uniform minute prices, which are still the de facto industry standard.

  • Christian Müller, Jochen Gönsch: Simulation zur Evaluation der Optimierung eines Bikesharing-Systems. In: Matthias Putz, Andreas Schlegel (Hrsg.): Simulation in Produktion und Logistik 2019. Wissenschaftliche Scripten, Auerbach 2019, S. 519-530. VolltextBIB DownloadDetails

    Bike sharing has been introduced in many cities, often by municipalities and is nowadays an established alternative for other short-distance transport systems. However, in cities with high elevations, the usual bike-sharing systems face a severe problem. Resulting from an imbalance of demand, the number of bikes at stations at elevated locations decreases during the day, while it increases at stations at lower locations. This situation poses a challenge for the relocation process because high numbers of bicycles have to be transported to the stations at elevated locations in order to achieve a suitable starting point for the next period. With the usage of e-bike sharing-systems, this problem can be circumvented because e-bikes facilitate the mobility in elevated and steep terrains. This paper considers an e-bike sharing-system with removable batteries. In the first step, a deterministic Mixed-Integer Linear Program (MILP) calculates the optimal route for trucks and the optimal initial distribution of bikes. In the second step, a stochastic simulation should evaluate these results.

  • Kruk, N.; Gönsch, J.: Shared Mobility Systeme – Mathematische Ansätze für Gestaltung und Betrieb. In: WiSt – Wirtschaftswissenschaftliches Studium , Jg. 46 (2017) Nr. 6, S. 9-14. BIB DownloadDetails


Airline Schedule Planning für Low Cost Carrier (abgeschlossen)

Fluggesellschaften sehen sich heute einem hohen Kostendruck ausgesetzt, welcher etwa durch die CO2-Abgabe weiter steigen wird. Dies zwingt sie, ihre Kapazitäten effizient zu nutzen. Voraussetzung dafür ist ein aus Sicht der potenziellen Passagiere möglichst attraktiver Flugplan, sowohl im Hinblick auf die angebotenen Verbindungen als auch die Flugzeiten. Die Gestaltung entsprechender Flugpläne auf mittelfristiger Planungsebene erweist sich dabei als äußerst komplexes Entscheidungsproblem, das nur mit Hilfe quantitativer Methoden erfolgreich angegangen werden kann.

Im Rahmen des Projekts werden schwerpunktmäßig folgende zwei Aspekte näher beleuchtet:

  • Wie lässt sich für einen gegebenen Flugplan die erwartete Nachfrage unter Berücksichtigung der kurzfristigen Effekte des Fleet Assignments und des Revenue Managements geeignet prognostizieren?
  • Welche der Teilprobleme lassen sich — insbesondere vor dem Hintergrund der speziellen Anforderungen eines Low Cost Carriers — simultan in einem integrierten Optimierungsmodell betrachten, und wie können diese Optimierungsprobleme gelöst werden?


  • Faust, O.; Gönsch, J.; Klein, R.: Demand-oriented Integrated Scheduling for Point-to-Point Airlines. In: Transportation Science, Jg. 51 (2017) Nr. 1, S. 196-213. PDFVolltextBIB DownloadDetails

    Optimizing an airline schedule usually comprises multiple planning stages. These are the choice of flights to offer (schedule design), the assignment of fleets to flight legs (fleet assignment), and the construction of rotations under consideration of maintenance constraints (aircraft maintenance routing). Moreover, the airline must assign crews to all flights (crew scheduling). Traditionally, either these scheduling stages are considered sequentially or an existing schedule is modified to cope with the arising complexity issue. More recently, some authors have developed models that integrate adjacent stages. In this paper, outcomes of a research project with airline information technology provider Lufthansa Systems are presented. We consider the case of a small to medium-sized point-to-point airline with a homogeneous fleet. Hence, fleet assignment is omitted, which offers the possibility to solve schedule design and aircraft maintenance routing simultaneously. Our approach explicitly accounts for passengers’ return flight demand and for marginal revenues declining with increasing seat capacity, hence, anticipating the effects of capacity control in revenue management systems. To solve the arising integrated mixed-integer problem, a branch-and-price approach and a column generation-based heuristic have been developed. An extensive numerical study, using data from a major European airline provided by Lufthansa Systems, shows that the presented approaches yield high-quality solutions to real-world problem instances within a reasonable time.

  • Gönsch, J.: Airline Schedule Planning — Grundlagen und aktuelle Entwicklungen. In: WiSt – Wirtschaftswissenschaftliches Studium (2010) Nr. 39, S. 230-235. BIB DownloadDetails