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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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.

Mehri Bagherian | Bi-Clique Finding | Best Researcher Award

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Assoc. Prof. Dr. Mehri Bagherian | Bi-Clique Finding | Best Researcher Award

Operations Research at University of Guilan, Iran📖

Dr. Mehri Bagherian is an Associate Professor of Applied Mathematics at the Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran. With extensive expertise in operations research, network flows, and applied mathematics, she has established herself as a distinguished researcher and academician. Over the years, Dr. Bagherian has made substantial contributions to advancing mathematical models and algorithms for complex problems, reflected in her numerous publications in high-impact journals.

Profile

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Education Background🎓

Dr. Bagherian holds a Ph.D. in Applied Mathematics, specializing in Network Flows, from the University of Tehran, Iran. She also earned her Master of Science in Applied Mathematics with a focus on Operations Research from Amir Kabir University of Technology, Tehran, and a Bachelor of Science in Applied Mathematics from the University of Tehran.

Professional Experience🌱

Dr. Bagherian has been an integral part of the University of Guilan, serving as an Associate Professor. Her academic and research pursuits focus on mathematical modeling, optimization, and the application of operations research to real-world problems. She has successfully supervised numerous research projects and has been an active contributor to the global scientific community through her extensive publication record.

Research Interests🔬

Dr. Bagherian’s research interests lie in network flows, binary linear programming, heuristic algorithms, game theory, multi-objective optimization, and bioinformatics. She has a particular focus on solving computational problems related to haplotype assembly, supply chain management, and cloud computing.

Author Metrics

Dr. Bagherian has published over 23 research articles in reputed international journals such as The Journal of Supercomputing, Computers & Industrial Engineering, BMC Bioinformatics, and Telecommunication Systems. Her work is widely recognized, with citations demonstrating the impact of her research on fields including applied mathematics, computational biology, and operations research.

Publications Top Notes 📄

1. “3D UAV Trajectory Planning Using Evolutionary Algorithms: A Comparison Study”

  • Authors: M. Bagherian, A. Alos
  • Journal: The Aeronautical Journal, 119 (1220), pp. 1271-1285
  • Year: 2015
  • Citations: 48
  • Summary: This study compares various evolutionary algorithms for 3D trajectory planning of unmanned aerial vehicles (UAVs). It provides valuable insights into optimizing flight paths for efficiency and safety.

2. “A New Model for Optimal TF/TA Flight Path Design Problem”

  • Authors: R. Zardashti, M. Bagherian
  • Journal: The Aeronautical Journal, 113 (1143), pp. 301-308
  • Year: 2009
  • Citations: 14
  • Summary: This work introduces an innovative model for Terrain Following (TF) and Terrain Avoidance (TA) flight path design, addressing critical challenges in aviation safety and navigation.

3. “A Multi-Objective Imperialist Competitive Algorithm (MOICA) for Finding Motifs in DNA Sequences”

  • Authors: S.A. Gohardani, M. Bagherian, H. Vaziri
  • Journal: Mathematical Biosciences and Engineering, 16 (3), pp. 1575-1596
  • Year: 2019
  • Citations: 11
  • Summary: This research applies a multi-objective optimization algorithm to identify motifs in DNA sequences, advancing computational biology and genetic research.

4. “Issues on DEA Network Models of Färe & Grosskopf and Kao”

  • Authors: R. Feizabadi, M. Bagherian, S.S. Moghadam
  • Journal: Computers & Industrial Engineering, 128, pp. 727-735
  • Year: 2019
  • Citations: 10
  • Summary: This paper addresses critical challenges and proposes refinements to Data Envelopment Analysis (DEA) network models, enhancing their application in efficiency analysis.

5. “Unmanned Aerial Vehicle Terrain Following/Terrain Avoidance/Threat Avoidance Trajectory Planning Using Fuzzy Logic”

  • Author: M. Bagherian
  • Journal: Journal of Intelligent & Fuzzy Systems, 34 (3), pp. 1791-1799
  • Year: 2018
  • Citations: 10
  • Summary: This study leverages fuzzy logic to optimize UAV trajectory planning, focusing on terrain and threat avoidance for increased mission success.

Conclusion

Assoc. Prof. Dr. Mehri Bagherian is an exceptional candidate for the Best Researcher Award. Her academic rigor, impactful publications, and innovative approaches make her a strong contender. To further strengthen her profile, she could focus on increasing her research’s interdisciplinary reach and broader societal impact. However, her contributions to mathematical modeling, optimization, and applied operations research are already commendable, and she is highly deserving of recognition for her achievements.

Emine Baş | Optimization Algorithms | Best Researcher Award

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Assoc. Prof. Dr. Emine Baş | Optimization Algorithms | Best Researcher Award

Author at Konya Technical University, Turkey📖

Dr. Emine Baş is a dedicated researcher and academic specializing in optimization algorithms, artificial intelligence, data mining, and machine learning. With a strong foundation in computer engineering and extensive experience in higher education, she has significantly contributed to both academia and applied research.

Profile

Scopus Profile

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Education Background🎓

  • Bachelor’s Degree (2006): Computer Engineering, Selçuk University
  • Master’s Degree (2013): Computer Engineering, Selçuk University (Thesis: RFID System Implementation and Application)
  • Doctorate (2020): Computer Engineering, Konya Technical University (Thesis: Performance Improvements in Continuous and Discrete Optimization Problems Using the Social Spider Algorithm)

Professional Experience🌱

Dr. Baş has been an instructor at Selçuk University since 2007. Initially appointed to Huğlu Vocational School, she transitioned to Kulu Vocational School in 2015, where she continues to educate and mentor students. She also holds administrative roles, such as Deputy Head of the Computer Technologies Department and ECTS Coordinator.

Research Interests🔬

Dr. Baş’s research focuses on swarm intelligence, heuristic algorithms, continuous and discrete optimization problems, artificial intelligence, database systems, machine learning, and big data analytics. She leverages these technologies to address complex optimization challenges and enhance data-driven decision-making.

Author Metrics

Dr. Baş has published extensively in high-impact journals such as Soft Computing and Expert Systems with Applications. Her work has received numerous citations, demonstrating her influence in fields like optimization and algorithm development. Her notable publications include advancements in binary social spider algorithms and their applications in feature selection and optimization tasks.

Publications Top Notes 📄

1. An Efficient Binary Social Spider Algorithm for Feature Selection Problem

  • Authors: Emine Baş, E. Ülker
  • Journal: Expert Systems with Applications, Vol. 146, Article 113185
  • Publication Year: 2020
  • Citations: 63
  • Summary: This paper introduces a binary social spider algorithm (SSA) tailored for feature selection problems. It demonstrates improved efficiency in selecting relevant features for machine learning tasks while maintaining solution quality.

2. A Binary Social Spider Algorithm for Uncapacitated Facility Location Problem

  • Authors: Emine Baş, E. Ülker
  • Journal: Expert Systems with Applications, Vol. 161, Article 113618
  • Publication Year: 2020
  • Citations: 51
  • Summary: This study applies the binary SSA to the uncapacitated facility location problem, achieving better performance in terms of cost and computational efficiency compared to traditional optimization methods.

3. Binary Aquila Optimizer for 0–1 Knapsack Problems

  • Author: Emine Baş
  • Journal: Engineering Applications of Artificial Intelligence, Vol. 118, Article 105592
  • Publication Year: 2023
  • Citations: 28
  • Summary: This paper presents a novel binary variant of the Aquila optimizer, addressing the 0–1 knapsack problem with improved accuracy and computational efficiency.

4. A Binary Social Spider Algorithm for Continuous Optimization Task

  • Authors: Emine Baş, E. Ülker
  • Journal: Soft Computing, Vol. 24(17), pp. 12953–12979
  • Publication Year: 2020
  • Citations: 26
  • Summary: The research adapts the SSA for continuous optimization tasks, showcasing its potential to solve complex mathematical problems with higher precision.

5. Improved Social Spider Algorithm for Large-Scale Optimization

  • Authors: Emine Baş, E. Ülker
  • Journal: Artificial Intelligence Review, Vol. 54(5), pp. 3539–3574
  • Publication Year: 2021
  • Citations: 22
  • Summary: This paper enhances the SSA for large-scale optimization problems, improving scalability and convergence rates, particularly for applications with high-dimensional datasets.

Conclusion

Dr. Emine Baş exemplifies excellence in research, academic mentorship, and innovation. Her impactful contributions to optimization algorithms, artificial intelligence, and machine learning position her as a deserving candidate for the Best Researcher Award.

With a strong academic foundation, proven research capabilities, and a focus on solving complex real-world problems, she has laid a robust groundwork for continued contributions to the field. Addressing areas such as broader collaborations and industrial engagement would further elevate her profile as a global leader in optimization and AI.