Dissertation award of the Operations Research Society (GOR e.V.), 2012

OptWare award of the “Initiative Wissenschaft und Automobilindustrie” (IWA e.V.), 2012

Dissertation award of the Economics Department, Friedrich Schiller University of Jena, 2012

OMEGA

International Transactions in Operational Research (Special Issue on “Sustainable and Responsive Transportation and Logistics”)

OMEGA (Special Issue on “Customized Assembly Systems”)

Alena Otto holds the Professorship in Advanced Analytics in Manufacturing Management at the Technical University of Munich, Campus Heilbronn.

Her research focuses on developing quantitative, data-driven optimization, and artificial intelligence (AI) methods to support decision-making in manufacturing. In particular, her work addresses:

• Optimization of data collection and validation,
• Integrated planning, and
• Multi-stage planning under uncertainty.

Alena Otto has authored numerous publications in highly ranked academic journals. She serves as an Associate Editor for OMEGA and regularly organizes areas, streams, and sessions at leading international operations research conferences. Her research has been recognized with several prestigious academic and industry awards.

Before joining TUM Heilbronn, she held the Chair of Management Science/Operations and Supply Chain Management at the University of Passau. She completed her postdoctoral Habilitation at the University of Siegen, where she previously worked as a postdoctoral fellow and served as interim professor for Business Administration/Quantitative Planning. She earned her doctorate in Management Science with highest honors from the University of Jena. Before pursuing her Ph.D., she worked for several years in strategic consultancy, leading projects in Ukraine, Germany, and Russia. She holds a master’s degree in economics from the EERC in Kyiv (Ukraine) and a bachelor’s degree in economics from the Belarusian State University in Minsk (Belarus), with a strong focus on quantitative methods.

Originally from Belarus, Alena Otto is married and has two children.

Selected current research projects / Research Areas

Learn2WIn – Optimizing Data Collection and Operations with the Help of Artificial Intelligence 
Manufacturing companies handle large volumes of low-quality data, the validation of which is costly, especially when high accuracy is required. This project specifically focuses on data related to precedence relations between tasks. It leverages optimization and AI technologies to identify and collect the most critical data, ensuring efficient manufacturing assembly within a limited time budget. 

KIMoNo – AI-based cross-type mobility optimization in rural regions 
is an interdisciplinary BMVI financed project. Specimen analysis in laboratories is a key component of healthcare infrastructure, playing a crucial role in diagnostics, disease monitoring, and evaluating treatment outcomes. Utilizing AI methods and optimization, our subproject examines and optimizes the operations of medical laboratories – from specimen transportation to laboratories, through the analysis process, and finally to the transmission of results back to the doctor.

SuPerPlan: Sustainable personnel planning for companies with fluctuating demand 

is a DFG-funded (414225725) research project. Even with fluctuating demand, it can be attractive for companies to conclude long-term employment contracts. The project SuPerPlan develops concepts for sustainable production management by using modern optimization techniques. 

Human-Centered Manufacturing 

In manufacturing, controlling ergonomic risks in the workplace is essential due to legal requirements, concern for workers' health, and economic considerations. This project explores human-centered operations management, focusing on cost-effective methods to reduce ergonomic risks through intelligent restructuring of operations, such as reallocating tasks among workers.

Optimization of warehousing operations under dynamically arriving orders. Competition in the e-commerce sector often revolves around fast (same-day or express) and low-cost deliveries. However, achieving both speed and cost efficiency is challenging, as faster delivery usually comes at the expense of economies of scale and consolidation. This study explores ways to enhance decision-making policies to minimize both costs and delivery times. Among other aspects, it evaluates the benefits of order accumulation (wait-and-see policies) and the potential advantages of anticipating future orders.

Key Publications

• Finnah, B., Gönsch, J., and Otto, A. (2025). Resolving the curse of poor data quality: Optimization perspective on data collection and validation. European Journal of Operational Research, 322, 740-752. https://doi.org/10.1016/j.ejor.2024.11.009  
• Otto, A., Golden, B., Lorenz, C., Luo, Y., and Pesch, E. (2024). On delivery policies for a truck-and-drone tandem in disaster relief. IISE Transactions, 1–17. https://doi.org/10.1080/24725854.2024.2410353
• Otto, A., and Tilk, C. (2024). Intelligent design of sensor networks for data-driven sensor maintenance at railways. OMEGA, 127, 103094. https://doi.org/10.1016/j.omega.2024.103094  
• Hosseini, A., Otto, A., and Pesch, E. (2024). Scheduling in manufacturing with transportation: Classification and solution techniques. European Journal of Operational Research, 315, 821–843. https://doi.org/10.1016/j.ejor.2023.10.013
• Schwerdfeger, S., Otto, A., and Boysen, N. (2021). Rail platooning: Scheduling trains along a rail corridor with rapid-shunting facilities. European Journal of Operational Research, 294, 760–778. https://doi.org/10.1016/j.ejor.2021.02.019
• Otto, A., and Li, X. (2020). Product sequencing in multiple-piece-flow assembly lines. OMEGA, 91, 102055. https://doi.org/10.1016/j.omega.2019.03.013  
• Otto, A., and Pesch, E. (2019). Algorithms for train-to-yard assignment at multiple railyards. OR Spectrum, 41, 549–580. https://doi.org/10.1007/s00291-019-00547-y
• Li, X., Otto, A., and Pesch, E. (2019). Solving the single crane scheduling problem at rail transshipment yards. Discrete Applied Mathematics, 264, 134–147. https://doi.org/10.1016/j.dam.2018.07.021
• Battaïa, O., Otto, A., Sgarbossa, F., and Pesch, E. (2018). Future trends in management and opera-tion of assembly systems: From customized assembly systems to cyber-physical systems. OMEGA, 78, 1–5. https://doi.org/10.1016/j.omega.2018.01.010
• Jaehn, F., Otto, A., and Seifried, K. (2018). Shunting operations at flat yards: Retrieving freight railcars from storage tracks. OR Spectrum, 40, 367–393. https://doi.org/10.1007/s00291-017-0495-x
• Otto, A., Agatz, N., Campbell, J., Golden, B., and Pesch, E. (2018). Optimization approaches for civil applications of unmanned aerial vehicles (UAVs), or drones: A survey. Networks, 72, 411−458. https://doi.org/10.1002/net.21818
• Otto, A., Boysen, N., Scholl, A., and Walter, R. (2017). Ergonomic workplace design in the fast pick area. OR Spectrum, 39, 945–975. https://doi.org/10.1007/s00291-017-0479-x
• Battini, D., Calzavara, M., Otto, A., and Sgarbossa, F. (2017). Preventing ergonomic risks with integrated planning on assembly line balancing and parts feeding. International Journal of Production Research, 55, 7452–7472. https://doi.org/10.1080/00207543.2017.1363427
• Otto, A., and Battaïa, O. (2017). Reducing physical ergonomic risks at assembly lines by line balancing and job rotation: A survey. Computers & Industrial Engineering, 111, 467–480. https://doi.org/10.1016/j.cie.2017.04.011
• Otto, A., and Pesch, E. (2017). Operation of shunting yards: Train-to-yard assignment problem. Journal of Business Economics, 87, 465–486. https://doi.org/10.1007/s11573-016-0827-3
• Otto, A., Li, X., and Pesch, E. (2016). Two-way bounded dynamic programming approach for operations planning in transshipment yards. Transportation Science, 51, 325–342. https://doi.org/10.1287/trsc.2016.0688
• Otto, C. and Otto, A. (2014). Extending assembly line balancing problem by incorporating learning effects. International Journal of Production Research, 52, 7193–7208. https://doi.org/10.1080/00207543.2014.910618
• Otto, C., and Otto, A. (2014). Multiple-source learning precedence graph concept for the automotive industry. European Journal of Operational Research, 234, 253–265. https://doi.org/10.1016/j.ejor.2013.09.034
• Otto, C., and Otto, A. (2014). How to design effective priority rules: Example of simple assembly line balancing. Computers & Industrial Engineering, 69, 43–52. https://doi.org/10.1016/j.cie.2013.12.013
• Otto, A., Otto, C., and Scholl, A. (2013). Systematic data generation and test design for solution algorithms on the example of SALBPGen for assembly line balancing. European Journal of Operational Research, 228, 33–45. https://doi.org/10.1016/j.ejor.2012.12.029
• Otto, A., and Scholl, A. (2012). Reducing ergonomic risks by job rotation scheduling. OR Spectrum, 35, 711–733. https://doi.org/10.1007/s00291-012-0291-6
• Otto, A., and Scholl, A. (2011). Incorporating ergonomic risks into assembly line balancing. European Journal of Operational Research, 212, 277–286. https://doi.org/10.1016/j.ejor.2011.01.056