Dr. Jarmo Haferkamp
Dr. Jarmo Haferkamp
Lehrstuhl BWL, insb. Management Science
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10/2023 |
Doctorate degree (summa cum laude) |
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Since 01/2018 |
Research associate, Chair of Management Science, Otto-von-Guericke-Universität Magdeburg |
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08/2016 - 11/2017 |
Working student, Internal Audit Department, ALBA Group |
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10/2015 - 12/2017 |
Master of Science in Information Systems, Freie Universität Berlin |
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11/2013 - 07/2015 |
Student assistant, Department of Information Technology, Universität Hamburg |
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10/2012 - 12/2015 |
Bachelor of Science in Information Systems, Universität Hamburg |
2024
Peer-reviewed journal article
Design of multi-optional pickup time offers in ride-sharing systems
Haferkamp, Jarmo
In: EURO journal on transportation and logistics - Amsterdam, Niederlande : Elsevier, Bd. 13 (2024), Artikel 100134
2023
Peer-reviewed journal article
Heatmap-based decision support for repositioning in ride-sharing systems
Haferkamp, Jarmo; Ulmer, Marlin W.; Ehmke, Jan Fabian
In: Transportation science - Hanover, Md. : INFORMS . - 2023 [Online first]
Dissertation
Demand management and vehicle routing in dynamic ride-sharing systems
Haferkamp, Jarmo; Ulmer, Marlin Wolf; Ehmke, Jan Fabian
In: Magdeburg: Universitätsbibliothek, Dissertation Otto-von-Guericke-Universität Magdeburg, Fakultät für Wirtschaftswissenschaft 2023, 1 Online-Ressource (168 Seiten, 8,05 MB) [Literaturverzeichnis: Seite 135-146][Literaturverzeichnis: Seite 135-146]
2022
Peer-reviewed journal article
Effectiveness of demand and fulfillment control in dynamic fleet management of ride-sharing systems
Haferkamp, Jarmo; Ehmke, Jan Fabian
In: Networks - New York, NY : Wiley, Bd. 79 (2022), Heft 3, S. 314-337
Non-peer-reviewed journal article
Heatmap-based decision support for repositioning in ride-sharing systems
Haferkamp, Jarmo; Ulmer, Marlin Wolf; Ehmke, Jan Fabian
In: Magdeburg: Otto-von-Guericke-Universität Magdeburg: Fakultät für Wirtschaftswissenschaft, 2022, 1 Online-Ressource (36 Seiten, 1,58 MB) - (Working paper series; Otto von Guericke Universität Magdeburg, Faculty of Economics and Management; 2022, no. 3)
2020
Peer-reviewed journal article
An efficient insertion heuristic for on-demand ridesharing services
Haferkamp, Jarmo; Ehmke, Jan Fabian
In: Transportation Research Procedia - Amsterdam [u.a.]: Elsevier, Bd. 47 (2020), S. 107-114
Article in conference proceedings
Potential of anticipatory decision-making in dynamic fleet management
Haferkamp, Jarmo; Ehmke, Jan Fabian
In: Konferenz: TSL Second Triennial Conference, Arlington, May 27-29, 2020, Proceedings of the TSL Second Triennial Conference - informs, 2020, Artikel TSL.2020.049, 4 Seiten
Non-peer-reviewed journal article
Evaluation of anticipatory decision-making in ride-sharing services
Haferkamp, Jarmo; Ehmke, Jan Fabian
In: Magdeburg: Otto-von-Guericke-Universität Magdeburg: Fakultät für Wirtschaftswissenschaft, Der Dekan, 2020, 1 Online-Ressource (28 Seiten, 5,78 MB) - (Working paper series; Otto von Guericke Universität Magdeburg, Faculty of Economics and Management; 2020, no. 4)
2019
Peer-reviewed journal article
Evaluation of delivery cost approximation for attended home deliveries
Köhler, Charlotte; Haferkamp, Jarmo
In: Transportation Research Procedia - Amsterdam [u.a.]: Elsevier, Bd. 37 (2019), S. 67-74
Current projects
Pro-Active Routing for Emergency Testing in Pandemics
Duration: 01.01.2023 bis 31.12.2025
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.
