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:

Scopus

Orcid

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.

Zahra Seyedzadeh | Supply chain | Best Researcher Award

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Ms. Zahra Seyedzadeh | Supply chain | Best Researcher Award

Zahra Seyedzadeh at Iran university of science and technology, Iran.

Zahra Sadat Seyedzadeh is a dedicated researcher and analyst specializing in industrial engineering, optimization, and systems analysis. She has contributed significantly to research projects in supply chain design, emergency medical services, and machine learning applications. As a distinguished researcher under Iran’s National Elites Foundation, she has been involved in impactful studies on sustainable supply chains, data analytics, and decision-making techniques.

Professional Profile:

Scopus

Education Background

  • Ph.D. in Industrial Engineering (Optimization & Systems Analysis) – Iran University of Science and Technology (2021–Present, Exceptional Talent Admission, GPA: 18.86/20)
  • M.Sc. in Industrial Engineering (Optimization & Systems Analysis) – Iran University of Science and Technology (2017–2019, GPA: 17.79/20)
  • B.Sc. in Industrial Engineering – Ershad University (2013–2017, GPA: 18/20)

Professional Development

Zahra has gained diverse experience across research and industry. She interned at the Institute for Research and Planning in Commerce and served as an Analyst at the Customs Administration of Iran. Recognized as a Distinguished Researcher under the Shahid Ahmadi Roshan Program, she has actively contributed to research and planning initiatives in commerce, logistics, and industrial systems.

Research Focus

Her research focuses on supply chain optimization, decision-making techniques, emergency medical services network design, data mining applications, and machine learning in supply chain forecasting. She has worked extensively on designing robust and sustainable supply chain networks under uncertainty, ranking suppliers, and preprocessing methods for diverse data types.

Author Metrics:

  • Publications in High-Impact Journals, including Science of the Total Environment (IF: 8.2, Q1) and Journal of Industrial and Systems Engineering (Q3)
  • Conference Presentations, including the 17th Iranian International Industrial Engineering Conference on emergency medical services and supply chain disruption management
  • Bibliometric Analysis Expertise using VOSviewer and CiteSpace

Awards and Honors:

  • Distinguished Researcher, Shahid Ahmadi Roshan Program, Iran’s National Elites Foundation
  • Exceptional Talent Admission, Ph.D. Program, Iran University of Science and Technology\

Publication Top Notes

1. Towards a Sustainable Viticultural Supply Chain under Uncertainty: Integration of Data Envelopment Analysis, Artificial Neural Networks, and a Multi-Objective Optimization Model

  • Journal: Science of the Total Environment (Impact Factor: 8.2, Q1)
  • Authors: Zahra Sadat Seyedzadeh, Mohammad Saeed Jabalameli, Ehsan Dehghani
  • Publication Year: 2025
  • Abstract: This study integrates data envelopment analysis (DEA), artificial neural networks (ANNs), and a multi-objective optimization model to develop a sustainable viticultural supply chain under uncertainty. It provides a robust framework for improving efficiency, resilience, and environmental sustainability in the wine industry.

2.  A Robust Scenario-Based Model for Locating Emergency Medical Services Bases

  • Journal: Journal of Industrial and Systems Engineering (Impact Factor: 0.694, Q3)
  • Authors: Zahra Sadat Seyedzadeh, Mohammad Saeed Jabalameli, Ehsan Dehghani
  • Abstract: This research proposes a robust scenario-based model for optimizing emergency medical services (EMS) base locations, addressing uncertainty, demand fluctuations, and resource allocation efficiency to enhance emergency response times and service coverage.

3.  Emergency Medical Services Network Design under Uncertainty

  • Conference: 17th Iranian International Industrial Engineering Conference
  • Authors: Zahra Sadat Seyedzadeh, Mohammad Saeed Jabalameli, Saeed Yaghoubi
  • Abstract: The study focuses on designing a resilient EMS network, incorporating stochastic demand modeling and robust optimization techniques to improve emergency response effectiveness in unpredictable environments.

4. Emergency Medical Services Supply Chain Network Design and Backup Services under Disruption

  • Conference: 17th Iranian International Industrial Engineering Conference
  • Authors: Zahra Sadat Seyedzadeh, Mohammad Saeed Jabalameli
  • Abstract: This paper examines EMS network vulnerabilities and proposes a backup service strategy to ensure continuity of care and resource allocation in cases of service disruptions due to disasters or infrastructure failures.

Conclusion

Zahra Sadat Seyedzadeh is a strong candidate for a Best Researcher Award, given her exceptional research output, interdisciplinary expertise, and recognized academic achievements. She has demonstrated high-impact research in supply chain optimization, EMS design, and machine learning applications, with publications in top-tier journals.

To further strengthen her research profile, she could focus on international collaborations, real-world applications of her models, and leadership roles in large research projects. Overall, her contributions to industrial engineering and systems optimization make her a highly deserving candidate for the award.

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:

Scopus

Orcid

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.

Nelson Martins | Circular Economy | Best Researcher Award

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Mr. Nelson Martins | Circular Economy | Best Researcher Award

Técnico at APICER, Portugal📖

Nelson Correia Martins is an experienced Production and Process Engineering Leader with a strong background in Lean Management, Industrial Optimization, and Process Improvement. With over 18 years of experience in manufacturing, process engineering, and production management, he has successfully led teams, optimized operations, and contributed to technological advancements in multiple industries, including ceramics, chemicals, pharmaceuticals, and manufacturing. Currently serving as a Process Engineer at APICER – Coimbra, he focuses on digital transformation, energy transition, circular economy, and process optimization.

Profile

Orcid Profile

Education Background🎓

  • Postgraduate Diploma in Lean Management, Comunidade Lean Thinking (2014-2015)
  • Master’s in Chemical Engineering, University of Aveiro (2007-2008)
  • Erasmus Program in Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain (2004-2005)
  • Bachelor’s in Chemical Engineering, University of Aveiro (2000-2005)
  • Award: Prémio Eng. António Pascoal – Best Engineering Student at the University of Aveiro (2000/2001)

Professional Experience🌱

Nelson has held key leadership roles across various industries, driving operational efficiency, team performance, and process optimization:

  • Process Engineer, APICER – Coimbra (2023 – Present)
    • Technical support in digitalization, energy transition, circular economy, and process optimization.
  • Production Director, NG- Oficina de Porcelanas – Aveiro (2021 – 2023)
    • Led teams to enhance productivity, quality, stock management, and cost optimization.
  • Production Manager, JADE Creation – Albergaria-a-Velha (2019 – 2021)
    • Managed manufacturing operations, Lean strategies, and MRP data analysis.
  • Lean Process Engineer, The Navigator Company – Cacia (2019)
    • Standardized paper tissue rewinder processes and improved workflow efficiency.
  • Production Director, Mesa Ceramics – Estarreja (2017 – 2018)
    • Managed industrial startup and production team leadership.
  • Production Manager, Sanitana (ROCA Group) – Anadia (2015 – 2017)
    • Led process optimization, KPI tracking, and team management.
  • Shift Manager, Riastone (Vista Alegre Group) – Ílhavo (2014 – 2015)
    • Implemented Lean methodologies (Toyota Kata, 5S, SMED) and KPI tracking.
  • R&D Engineer, Bresfor (FINSA Group) – Ílhavo & Pontevedra, Spain (2008 – 2014)
    • Conducted research, quality control, and regulatory compliance (REACH, formaldehyde reduction).
  • Process Engineer (Inov Contacto), Hovione – Lisbon & New Jersey, USA (2005 – 2007)
    • Worked on Good Manufacturing Practices (GMP), process optimization, and nanotechnology applications.
Research Interests🔬

Research interests include:

  • Lean Manufacturing & Continuous Improvement
  • Industrial Digitalization & Smart Manufacturing
  • Process Optimization & Energy Efficiency
  • Sustainable Manufacturing & Circular Economy

Author Metrics

  • Publications & Technical Contributions: Research in Lean Management, Process Optimization, and Industrial Digitalization.
  • Conference Presentations: Contributions to manufacturing and process engineering forums.
Awards and Honors

Prémio Eng. António Pascoal – Best Engineering Student at the University of Aveiro (2000/2001)

Publications Top Notes 📄

1. Tracking Secondary Raw Material Operational Framework—DataOps Case Study

  • Type: Journal Article
  • Field: Ceramics, DataOps, Industrial Process Optimization
  • Publication Date: January 28, 2025
  • DOI: 10.3390/ceramics8010012
  • Contributors: Gabriel Pestana, Marisa Almeida, Nelson Martins
  • Source: Crossref
  • Summary:
    This study presents an operational framework using DataOps principles to enhance secondary raw material tracking in ceramic manufacturing. It focuses on data-driven decision-making, process automation, and supply chain efficiency, contributing to sustainability and waste reduction in industrial operations.

2. Tracking Secondary Raw Material Operational Framework – DataOps Case Study (Preprint)

  • Type: Preprint
  • Publication Date: December 24, 2024
  • DOI: 10.20944/preprints202412.2007.v1
  • Contributors: Gabriel Pestana, Marisa Almeida, Nelson Martins
  • Source: Crossref
  • Summary:
    This preprint version outlines an innovative DataOps-based framework for monitoring and optimizing secondary raw material usage in industrial production. The approach integrates real-time data analytics, automation, and sustainability principles to improve resource efficiency in the ceramics industry.

Conclusion

Nelson Correia Martins is a highly deserving candidate for the Best Researcher Award, given his groundbreaking work in circular economy, Lean process optimization, and industrial digitalization. His research has direct applications in sustainable manufacturing, contributing to a waste-free, energy-efficient future.

With continued advancements in AI-driven sustainability, international collaboration, and industry adoption, his work can further revolutionize sustainable production models worldwide.

This nomination is strongly recommended based on his research impact, technical leadership, and commitment to industrial innovation.