Markus Rabe | Logistics | Pioneering Contribution Award

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Prof. Dr. Markus Rabe | Logistics | Pioneering Contribution Award

Professor at TU Dortmund University, Germany

Prof. Dr. Markus Rabe is a renowned academic and researcher in the field of IT in Production and Logistics. He currently serves as a Professor at the Faculty of Mechanical Engineering at TU Dortmund University, Germany. With over three decades of experience in applied research, he has significantly contributed to logistics simulation, supply chain modeling, and digital transformation in production systems.

Professional Profile:

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Education Background

  • Diploma in Physics, University of Konstanz, Germany

  • Doctor of Engineering (Dr.-Ing.), Technical University of Berlin, Germany

Professional Development

Prof. Rabe began his professional career in 1986 at Fraunhofer Institute for Production Systems and Design Technology (IPK), Berlin, where he held various senior roles including Head of the Department for Enterprise Processes and Logistics, Head of IT, and a member of the institute’s leadership circle. He has lectured at the Beuth University of Applied Sciences and TU Berlin. In 2010, he established the Department for IT in Production and Logistics at TU Dortmund University, where he also introduced a new master’s specialization. He is a board member of the Graduate School of Logistics, Dortmund, and has served as coordinator or lead in numerous European R&D projects involving simulation, distributed modeling, supply chain optimization, and enterprise network management.

Research Focus

  • Logistics and supply chain simulation

  • Digital twins and cyber-physical systems

  • Sustainable transportation and smart logistics

  • Material flow modeling and simulation

  • Verification and validation automation

  • Predictive maintenance and energy-efficient logistics

  • Decision support systems using simheuristics and fuzzy models

Author Metrics:

  • Over 100 peer-reviewed publications, including books, journal articles, and conference papers

  • Frequently published in Algorithms, Simulation Modelling Practice and Theory, International Journal of Computer Integrated Manufacturing, and Journal of Simulation

  • Contributor and editor of Springer and Palgrave Macmillan volumes

  • Regular presenter at top-tier conferences such as the Winter Simulation Conference (WSC) and Simulation in Production and Logistics (SPL)

Awards and Honors:

  • Key contributor and chair of the European project cluster “Ambient Intelligence Technologies for the Product Life Cycle (AITPL)”

  • Coordinator of the European IMS MISSION project (EU module)

  • Member of several prestigious academic and research committees in logistics and IT systems

  • Influential figure in shaping educational and research infrastructure at TU Dortmund and across European logistics research networks

Publication Top Notes

📦 1. The Deployment of Automated Parcel Lockers in Urban Logistics: Notions, Planning Principles, and Applications

  • Authors: Jorge Chicaiza Vaca, Markus Rabe, Jesús González-Feliu

  • Year: 2024

  • Source: Chapter in Theories and Practices for Sustainable Urban Logistics

  • Summary: This chapter explores the implementation of Automated Parcel Lockers (APLs) as a last-mile delivery solution. It introduces a combined simulation-optimization approach using a System Dynamics Simulation Model (SDSM) and a Facility Location Problem (FLP) model. The methodology is applied to a case study in Dortmund, Germany, evaluating three demand scenarios over a 60-month period. The study assesses both functional indicators (e.g., number of lockers and coverage) and economic indicators (e.g., Net Present Value) to guide third-party logistics providers in decision-making.

🔧 2. Combining Simulation and Recurrent Neural Networks for Model-Based Condition Monitoring of Machines

  • Authors: Alexander Wuttke, Markus Rabe, Joachim Hunker, Jan Philipp Diepenbrock

  • Year: 2024

  • Source: Proceedings of the Winter Simulation Conference (WSC ’24)

  • Summary: This paper presents a hybrid approach that integrates simulation models with Recurrent Neural Networks (RNNs) for condition-based maintenance of industrial machines. By combining the predictive capabilities of simulation with the pattern recognition strengths of RNNs, the methodology enhances the accuracy of machine condition monitoring. The approach is demonstrated through an industrial case study involving vacuum processes in furnaces.

📈 3. The Role of Simulation as a Method for Sales Forecasting – A Systematic Literature Review

  • Authors: Tobias Klima, Markus Rabe, Michael Toth

  • Year: [Year not specified]

  • Summary: This paper conducts a systematic literature review to examine the application of simulation methods in sales forecasting. It categorizes various simulation techniques and assesses their effectiveness in predicting sales, providing insights into best practices and identifying areas for future research.​

🔥 4. Utilizing Data Analysis for Optimized Determination of the Current Operational State of Heating Systems

  • Authors: Ahmed Qarqour, Sahil Jai Arora, Gernot J.P. Heisenberg, Markus Rabe, Tobias Kleinert

  • Year: [Year not specified]

  • Summary: This study focuses on the application of data analysis techniques to monitor and optimize the operational state of heating systems. By analyzing real-time data, the methodology aims to enhance energy efficiency and system reliability, contributing to more sustainable building management practices.​

🧭 5. Modeling of Logistics Networks with Labeled Property Graphs for Simulation in Digital Twins

  • Authors: Alexander Wuttke, Joachim Hunker, Anne Antonia Scheidler, Markus Rabe

  • Year: 2024

  • Source: Chapter in Simulation for a Sustainable Future (EUROSIM 2023)

  • Summary: This paper introduces a modeling framework that utilizes labeled property graphs to represent logistics networks within digital twins. The approach facilitates simulation, optimization, and monitoring tasks by providing a unified data model. A real-world case study in city logistics demonstrates the framework’s applicability and benefits in enhancing the accuracy and efficiency of logistics simulations.

Conclusion

Prof. Dr. Markus Rabe exemplifies what it means to pioneer innovation in logistics research. His seminal contributions to simulation-based logistics, integration of AI in predictive maintenance, and development of digital twins for sustainable supply chains demonstrate a transformational impact on both the academic community and practical logistics systems worldwide.

His visionary leadership in education, research, and European-level innovation projects makes him an ideal candidate for the Research for Pioneering Contribution Award in Logistics. Recognizing his work will not only honor decades of groundbreaking contributions but also inspire the next generation of logistics researchers and digital system innovators.

Pardis Roozkhosh | Supply Chain | Best Researcher Award

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Dr. Pardis Roozkhosh | Supply Chain | Best Researcher Award

Lecturer at Ferdowsi University of Mashhad, Iran.

Dr. Pardis Roozkhosh is a distinguished researcher and academic in industrial management and operations research, with expertise in resilient supply chains, additive manufacturing, and machine learning applications. She has an extensive background in optimization, logistics, and decision-making systems, with numerous research contributions and teaching experience in prestigious institutions across Iran. Her work integrates advanced computational techniques with industrial engineering solutions, making a significant impact in academia and applied research.

Professional Profile:

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Google Scholar

Education Background

  • Ph.D. in Industrial Management (Operations Research) – Ferdowsi University of Mashhad, Iran (2024)
    • Thesis: Resilient Supply Chain with Additive Manufacturing Capability Using Machine Learning Approach
    • GPA: 4.0/4.0 (19.04/20)
  • M.S. in Industrial Engineering (Systems Optimization) – Sadjad University of Mashhad, Iran (2018)
    • Thesis: Solving Partial Inspection Problems in Multistage Systems Using Double Sampling Plans Considering Uncertainty in Costs
  • B.S. in Industrial Engineering – Birjand University of Technology, Iran (2015)

Professional Development

Dr. Roozkhosh has held teaching and research positions at Ferdowsi University of Mashhad, Allameh Tabataba’i University, Sadjad University of Mashhad, and Kosar University of Bojnourd. She has taught statistics, probability theory, financial management, inventory control, and mathematics, guiding students in industrial management and engineering disciplines.

As a Research Assistant at Ferdowsi University of Mashhad since 2020, she has worked on logistics and transportation projects, including a study on Razavi Khorasan’s logistics systems. Additionally, she collaborated with the University of Sydney on Agrovoltaic modeling, optimizing solar panel applications in agricultural settings.

Research Focus

  • Resilient Supply Chain Management
  • Operations Research and Optimization Techniques
  • Machine Learning and Artificial Intelligence in Industrial Systems
  • Logistics and Transportation Modeling
  • Additive Manufacturing and Smart Production Systems
  • Simulation Techniques (Monte-Carlo, System Dynamics, etc.)

Author Metrics:

  • Google Scholar: [Profile Link]
  • h-Index: [Current h-Index]
  • Total Citations: [Number of Citations]
  • Reviewed Papers: IEEE Access, Journal of Simulation, International Journal of Productivity and Performance Management

Awards and Honors:

  • Alborz Prize (Iranian Nobel Prize) – 2024 (Oldest and most prestigious academic award in Iran)
  • Ranked in the Top 5% of Students in Iran’s Ph.D. Entrance Exam (Conquer Exam) – 2019
  • Executive Assistant at Journal of Systems Thinking in Practice (JSTINP) – 2022
  • Designed and Registered a Board Game Based on Optimization Algorithms – 2021

Publication Top Notes

1. MLP-based Learnable Window Size for Bitcoin Price Prediction

  • Authors: S. Rajabi, P. Roozkhosh, N. M. Farimani
  • Journal: Applied Soft Computing
  • Citations: 59
  • Year: 2022
  • Summary: This study utilizes a Multi-Layer Perceptron (MLP)-based model to dynamically adjust window sizes for improved Bitcoin price prediction using deep learning techniques.

2. Blockchain Acceptance Rate Prediction in the Resilient Supply Chain with Hybrid System Dynamics and Machine Learning Approach

  • Authors: P. Roozkhosh, A. Pooya, R. Agarwal
  • Journal: Operations Management Research 16 (2), 705-725
  • Citations: 49*
  • Year: 2023
  • Summary: This research integrates system dynamics and machine learning to predict blockchain adoption rates in resilient supply chains, enhancing digital transformation strategies.

3. A New Supply Chain Design to Solve Supplier Selection Based on Internet of Things and Delivery Reliability

  • Authors: A. Modares, M. Kazemi, V. B. Emroozi, P. Roozkhosh
  • Journal: Journal of Industrial and Management Optimization 19 (11), 7993-8028
  • Citations: 37
  • Year: 2023
  • Summary: The study presents a novel IoT-enabled supply chain model, improving supplier selection and delivery reliability through optimization techniques.

4. Partial Inspection Problem with Double Sampling Designs in Multi-Stage Systems Considering Cost Uncertainty

  • Authors: T. H. Hejazi, P. Roozkhosh
  • Journal: Journal of Industrial Engineering and Management Studies 6 (1), 1-17
  • Citations: 21
  • Year: 2019
  • Summary: This work introduces a double sampling inspection strategy in multi-stage production systems, addressing cost uncertainties in quality control.

5. Designing a New Model for the Hub Location-Allocation Problem Considering Tardiness Time and Cost Uncertainty

  • Authors: P. Roozkhosh, N. Motahari Farimani
  • Journal: International Journal of Management Science and Engineering Management 18 (1)
  • Citations: 20
  • Year: 2023
  • Summary: A mathematical optimization model is proposed for hub location-allocation problems, factoring in time delays and cost variability in logistics networks.

Conclusion

Dr. Pardis Roozkhosh is an outstanding researcher in resilient supply chains, AI applications in industrial systems, and logistics optimization. Her Alborz Prize, high-impact publications, interdisciplinary expertise, and leadership in academic peer review make her a strong candidate for the Best Researcher Award.

To further strengthen her profile, she can enhance international collaborations, increase citations, secure more research funding, and actively participate in global conferences. Given her achievements and ongoing contributions, she is well-deserving of this award and is a leading academic in industrial management and operations research.