Wan Li | Anomaly Detection | Best Researcher Award

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Dr. Wan Li | Anomaly Detection | Best Researcher Award

Research Staff at Oak Ridge National Laboratory, United States

Dr. Wan Li is a Research Associate at Oak Ridge National Laboratory (ORNL) specializing in transportation systems, with a focus on urban traffic network optimization, autonomous vehicle integration, and transportation energy analytics. He holds a Ph.D. in Civil and Environmental Engineering from the University of Washington, along with a Master’s and Bachelor’s in Civil and Environmental Engineering and Traffic Engineering, respectively. Dr. Li’s research includes modeling, simulation, and applying AI to optimize traffic control systems and improve energy efficiency. He has led several high-impact projects funded by the U.S. Department of Energy (DOE) and has contributed to innovations in connected and autonomous vehicle systems. He has received multiple awards, including the Best Paper Award at VEHICULAR 2021 and recognition as an Outstanding Reviewer. Dr. Li has authored numerous influential publications in top transportation journals, advancing the field of smart and sustainable transportation systems.

 

Profile

ORCID

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Education

Dr. Wan Li has an outstanding academic background that underpins his expertise in transportation systems and data-driven solutions. He earned his Ph.D. in Civil and Environmental Engineering from the University of Washington (2016-2019), where he specialized in advanced modeling and optimization techniques for urban traffic networks. Prior to that, he completed his M.S. in Civil and Environmental Engineering at Louisiana State University (2012-2014), where he gained a solid foundation in transportation systems engineering. His academic journey began with a B.S. in Traffic Engineering from Sun Yat-sen University (2008-2012), where he developed his initial interest in mobility and traffic control. This robust educational trajectory has significantly contributed to his innovative research and professional achievements in the field of transportation engineering.

Experience

Currently, Dr. Li is a Research Associate Staff at Oak Ridge National Laboratory (2022-Present), where he leads and contributes to groundbreaking projects funded by the U.S. Department of Energy (DOE). These include initiatives like AI-based adaptive signal timing optimization for urban areas, fleet electrification, and the development of EV ecosystems in economically distressed regions. As a Lead Researcher, he has driven innovative projects such as the deployment of AI for electric motor design, the use of foundation models for time-series predictions, and optimized mobility through signal coordination and CAV integration. His previous roles include Postdoctoral Researcher at Oak Ridge National Laboratory (2019-2022), AI Engineer at Huawei, and Algorithm Engineer at DiDi, each emphasizing his expertise in data-driven traffic optimization and ML-based solutions.

Research Interests

Dr. Wan Li’s research focuses on the modeling, simulation, and optimization of urban traffic networks, including the design and optimization of traffic control systems incorporating Connected and Autonomous Vehicles (CAVs). He also specializes in transportation big data analytics using machine learning (ML) and artificial intelligence (AI) and is deeply invested in transportation energy solutions, showcasing a strong commitment to sustainable and efficient mobility systems.

Skills

Dr. Wan Li possesses a wide range of advanced skills in transportation systems, AI, and energy optimization. His expertise includes modeling, simulation, and optimization of urban traffic networks, with a focus on integrating connected and autonomous vehicles (CAVs) into traffic control systems. He is proficient in applying machine learning (ML) and artificial intelligence (AI) for transportation big data analytics, energy efficiency, and mobility optimization. Dr. Li is highly skilled in traffic signal control, with experience in AI-based adaptive signal timing and the development of innovative systems to reduce congestion and energy consumption. His work in transportation energy and sustainable mobility also extends to electric vehicle (EV) ecosystem development and green economy initiatives. Additionally, Dr. Li has strong project management skills, having led multiple high-impact research initiatives funded by the U.S. Department of Energy (DOE) and other leading organizations. His technical proficiency is complemented by a deep understanding of the interdisciplinary aspects of urban mobility, AI, and energy systems.

 

Publication

  • Title: Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections
    Authors: B. Xu, XJ. Ban, Y. Bian, W. Li, J. Wang, SE. Li, K. Li
    Journal: IEEE Transactions on Intelligent Transportation Systems
    Volume: 20, Issue: 4, Pages: 1390-1403
    Cited by: 274
    Year: 2018
  • Title: Connected vehicles based traffic signal timing optimization
    Authors: W. Li, X. Ban
    Journal: IEEE Transactions on Intelligent Transportation Systems
    Volume: 20, Issue: 12, Pages: 4354-4366
    Cited by: 104
    Year: 2019
  • Title: Two-stream multi-channel convolutional neural network for multi-lane traffic speed prediction considering traffic volume impact
    Authors: R. Ke, W. Li, Z. Cui, Y. Wang
    Journal: Transportation Research Record
    Volume: 2674, Issue: 4, Pages: 459-470
    Cited by: 99
    Year: 2020
  • Title: Dynamic traffic signal timing optimization strategy incorporating various vehicle fuel consumption characteristics
    Authors: J. Zhao, W. Li, J. Wang, X. Ban
    Journal: IEEE Transactions on Vehicular Technology
    Volume: 65, Issue: 6, Pages: 3874-3887
    Cited by: 89
    Year: 2015
  • Title: Short-term traffic state prediction from latent structures: accuracy vs. efficiency
    Authors: W. Li, J. Wang, R. Fan, Y. Zhang, Q. Guo, C. Siddique, XJ. Ban
    Journal: Transportation Research Part C: Emerging Technologies
    Volume: 111, Pages: 72-90
    Cited by: 83
    Year: 2020

Conclusion

Dr. Wan Li’s multidisciplinary expertise in traffic engineering, ML/AI-driven solutions, and energy-efficient transportation systems, along with his impactful research and leadership in transformative projects, make him a strong contender for the Research for Best Researcher Award. His work not only advances academic knowledge but also contributes significantly to solving real-world transportation and sustainability challenges.

Boyu Wang | Cybersecurity | Best Researcher Award

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Dr. Boyu Wang | Cybersecurity | Best Researcher Award

Assistant professor at Beijing University of Civil Engineering and Architecture, China

Dr. Boyu Wang is a Principal Data Scientist at Tacoma Public Utilities in Washington, USA, where he leads energy and peak forecasting, financial modeling, and power operations. He holds a Ph.D. in Electrical Engineering from Louisiana State University, as well as Master’s degrees in Electrical Engineering and Computer Science. Dr. Wang has extensive experience in applying advanced data science techniques, including deep learning and blockchain, to optimize energy management systems. His research focuses on power flow prediction, decentralized micro-grids, and grid stability, and he has contributed to several publications and patents. He is committed to enhancing energy stability and resiliency through innovative data-driven solutions.

 

Profile

SCOPUS

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Education

Dr. Boyu Wang holds a Ph.D. in Electrical Engineering from Louisiana State University, Baton Rouge, LA, USA, which he completed from August 2014 to August 2018. Prior to his doctoral studies, he earned a Master of Science in Electrical Engineering from the same institution, Louisiana State University, during the period from August 2012 to August 2014. In addition to his background in electrical engineering, Dr. Wang expanded his expertise by obtaining a Master of Science in Computer Science from the Georgia Institute of Technology, where he studied from January 2020 to May 2022. His multidisciplinary academic training provides a solid foundation for his research and contributions in energy systems, machine learning, and data science.

 Experience

Dr. Boyu Wang currently serves as a Principal Data Scientist at Tacoma Public Utilities in Washington, USA, where he leads the annual energy and peak forecasting for resource planning, financial modeling, and power operations. His responsibilities include supporting various teams with data collection, cleaning, and manipulation, as well as developing risk models and automation tools to improve reporting efficiency. Dr. Wang has played a key role in assisting with project management strategies and energy stability efforts. Prior to this, he worked as a Power Engineer Intern at Entergy, where he conducted analysis on renewable energy integration into distribution systems. Throughout his career, Dr. Wang has leveraged his expertise in deep learning, blockchain, and data science to contribute to various innovative research projects, particularly in energy management and grid stability.

Research Interests

Dr. Boyu Wang’s research interests lie at the intersection of energy systems, machine learning, and advanced technologies. His work primarily focuses on applying deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM) models, to predict power flow and optimize grid stability in real-time. Additionally, Dr. Wang has explored blockchain-based energy management for decentralized micro-grids, developing dynamic pricing strategies and decision-making algorithms to enhance energy distribution and trading. He is also passionate about developing novel methods for power grid stability, including multi-layer constrained spectral clustering for post-contingency problems and dynamic programming-based control systems for micro-grids. His research aims to advance the resilience and efficiency of energy systems through the integration of cutting-edge computational techniques.

Skills

Dr. Boyu Wang possesses a robust skill set in programming, data analysis, and energy systems optimization. He is proficient in Python, R, and SQL, which he utilizes for data manipulation, analysis, and model development. Dr. Wang is also experienced with platforms and software such as Tableau, DBeaver, Snowflake, and Databricks, enabling him to work efficiently with large datasets and develop impactful visualizations and analytics solutions. His technical expertise extends to deep learning, where he has applied convolutional neural networks (CNN) and long short-term memory (LSTM) models for power flow prediction, as well as blockchain technology for decentralized energy management in micro-grids. These skills, combined with his background in electrical and computer engineering, allow him to tackle complex challenges in energy systems and grid stability.

 

Publication

Cybersecurity Enhancement of Power Trading within the Networked Microgrids Based on Blockchain and Directed Acyclic Graph Approach

  • Authors: B. Wang, M. Dabbaghjamanesh, A. Kavousi-Fard, S. Mehraeen
  • Journal: IEEE Transactions on Industry Applications
  • Volume: 55, Issue 6, Pages 7300-7309
  • Publication Year: 2019
  • Cited by: 188

A Novel Two-Stage Multi-Layer Constrained Spectral Clustering Strategy for Intentional Islanding of Power Grids

  • Authors: M. Dabbaghjamanesh, B. Wang, A. Kavousi-Fard, S. Mehraeen, …
  • Journal: IEEE Transactions on Power Delivery
  • Volume: 35, Issue 2, Pages 560-570
  • Publication Year: 2019
  • Cited by: 70

Blockchain-Based Stochastic Energy Management of Interconnected Microgrids Considering Incentive Price

  • Authors: M. Dabbaghjamanesh, B. Wang, A. Kavousi-Fard, N.D. Hatziargyriou, …
  • Journal: IEEE Transactions on Control of Network Systems
  • Volume: 8, Issue 3, Pages 1201-1211
  • Publication Year: 2021
  • Cited by: 43

Networked Microgrid Security and Privacy Enhancement by the Blockchain-Enabled Internet of Things Approach

  • Authors: M. Dabbaghjamanesh, B. Wang, S. Mehraeen, J. Zhang, A. Kavousi-Fard
  • Conference: 2019 IEEE Green Technologies Conference (GreenTech)
  • Pages: 1-5
  • Publication Year: 2019
  • Cited by: 37

Superconducting Fault Current Limiter Allocation in Reconfigurable Smart Grids

  • Authors: A.S. Abdollah Kavousi-Fard, Boyu Wang, Omid Avatefipour, Morteza …
  • Conference: IEEE, Berkley University Conference on Smart City and Smart Grid
  • Publication Year: 2019
  • Cited by: 28

Stability Improvement of Microgrids Using a Novel Reduced UPFC Structure via Nonlinear Optimal Control

  • Authors: H. Saberi, S. Mehraeen, B. Wang
  • Conference: 2018 IEEE Applied Power Electronics Conference and Exposition (APEC)
  • Pages: 3294-3300
  • Publication Year: 2018
  • Cited by: 17

Conclusion

Dr. Boyu Wang’s extensive work in energy systems, innovative applications of deep learning and blockchain technologies, and his leadership in power grid optimization make him an excellent candidate for the Research for Best Researcher Award. His research not only advances theoretical knowledge but also provides practical solutions for improving energy efficiency, grid stability, and resilience, aligning with the award’s recognition of impactful, cutting-edge research.

Sneha Mandal | Chemistry | Best Researcher Award

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Ms. Sneha Mandal | Chemistry | Best Researcher Award

Int.-PhD at Indian Institute of Science Education and Research (IISER), Tirupati, India

Ms. Sneha Mandal is a dedicated researcher currently pursuing an integrated Ph.D. in Chemistry at the Indian Institute of Science Education and Research (IISER), Tirupati, India. Her research focuses on nano-composite design and van der Waals gap modulation for advanced energy applications. She holds an M.S. in Chemistry from IISER Tirupati and a B.Sc. in Chemistry (Hons.) from P.K. Roy Memorial College, Dhanbad, where she graduated with first-class distinction. Her expertise spans electrochemistry, materials science, and advanced energy storage systems, with hands-on experience in cutting-edge techniques and instruments. Ms. Mandal has co-authored multiple research publications and actively participates in international conferences and workshops, contributing to advancements in energy storage technologies.

 

Profile

SCOPUS

ORCID

Education

Ms. Sneha Mandal is currently pursuing an integrated Ph.D. in Chemistry at the Indian Institute of Science Education and Research (IISER), Tirupati, India, since 2019. Her doctoral research focuses on “Nano-Composite Design: Van der Waals Gap Modulation for Advanced Energy Applications” under the supervision of Prof. Vijayamohanan K. Pillai. As part of this program, she has also completed an M.S. in Chemistry at IISER Tirupati with a CGPA of 7.2. Prior to this, she earned her Bachelor of Science in Chemistry (Hons.) with first-class distinction from P. K. Roy Memorial College, affiliated with Binod Bihari Mahato Koylanchal University, Dhanbad, Jharkhand, India, in 2019. Her progressive academic journey reflects her commitment to excellence and her strong foundation in the field of chemistry.

 Experience

Ms. Sneha Mandal is an accomplished researcher with extensive experience in advanced energy applications and materials science. Currently, she is pursuing an integrated Ph.D. at the Indian Institute of Science Education and Research (IISER), Tirupati, under the guidance of Prof. Vijayamohanan K. Pillai. Her doctoral research focuses on nano-composite design and van der Waals gap modulation for energy storage systems, particularly solid-state and Na-/Li-ion batteries. Ms. Mandal has honed her skills through hands-on experience with state-of-the-art instruments such as battery testers, electrochemical workstations, Raman spectroscopy, XRD, and many others. She has demonstrated expertise in electrochemical and galvanostatic measurements, coin-cell fabrication, and data analysis for cutting-edge research. Additionally, her active participation in workshops and training sessions on electrochemistry and cryo-electron microscopy highlights her commitment to professional development. Her contributions include co-authoring high-impact research publications in journals like Scientific Reports and Energy Advances and presenting her findings at esteemed international conferences. These experiences collectively reflect her deep engagement with advancing technologies in energy storage and materials science.

Research Interests

Ms. Sneha Mandal’s research interests lie at the intersection of electrochemistry, materials science, and advanced energy storage systems. She is particularly focused on the development and optimization of solid-state batteries, including Na-ion, Li-ion, Li-S, and Na-S batteries. Her expertise extends to the study and application of 2D materials, solid electrolytes, and advanced nanomaterials for enhancing battery performance. Ms. Mandal is deeply engaged in exploring electrocatalysis and the modulation of van der Waals gaps in nano-composites to advance energy applications. Her work also involves designing novel materials and improving the efficiency and stability of energy storage devices. Through her research, she aims to contribute to sustainable and high-performance energy solutions.

Skills

Ms. Sneha Mandal possesses hands-on expertise in advanced instrumentation and techniques essential for materials science and electrochemistry research. She is proficient in operating battery testers (BCS 805), electrochemical workstations (Solartron 1470E, Biologic SP 200), and conducting cyclic voltammetry, impedance spectroscopy, and galvanostatic measurements. Her skillset includes advanced spectroscopy and imaging techniques such as RAMAN, XRD, AFM, UV-Visible spectroscopy, and HR-TEM, along with coin-cell fabrication and glove box handling. Ms. Mandal is adept at data analysis and interpretation across a range of tools, making her well-equipped to address complex challenges in energy storage and materials development.

 

Publication

Van der Waals Gap Modulation of Graphene Oxide through Mono-Boc Ethylenediamine Anchoring for Superior Li-Ion Batteries

  • Authors: Mandal, S., Pillai, V.K., Ranjana Ponraj, M., Thavasi, V., Renugopalakrishnan, V.
  • Journal: Energy Advances
  • Volume: 3, Issue 8, Pages 1977–1991
  • Publication Year: 2024
  • Access: Open Access
  • Citations: 0

Interlayer, Gallery-Engineered Graphene Oxide Using Selective Protection of Mono-Boc-Ethylenediamine as Anode for Sodium Ion Batteries

  • Authors: Thushara, K.M., Ponraj, M.R., Mandal, S., Pillai, V.K., Bhagavathsingh, J.
  • Journal: Journal of Energy Storage
  • Volume: 73, Article 109237
  • Publication Year: 2023
  • Citations: 1

Effect of Hetero-Atom Doping on the Electrocatalytic Properties of Graphene Quantum Dots for Oxygen Reduction Reaction

  • Authors: Goswami, M., Mandal, S., Pillai, V.K.
  • Journal: Scientific Reports
  • Volume: 13, Article 5182
  • Publication Year: 2023
  • Citations: 12
  • Access: Open Access

Conclusion

Ms. Sneha Mandal’s robust academic background, cutting-edge research, technical proficiency, and impactful publications make her an exemplary candidate for the Best Researcher Award. Her dedication to advancing knowledge in energy materials and nanotechnology positions her as a promising scientist and deserving recipient of this honor.

FAN Bo | Data Security Management | Best Researcher Award

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Dr. FAN Bo | Data Security Management | Best Researcher Award

Senior Engineer at Southwest Jiaotong University, China

Dr. Fan Bo is a Senior Engineer and Chief Technical Specialist at the National Engineering Laboratory for Industrial Big Data Application Technology in China. With a Ph.D. and extensive experience in industrial innovation, he also serves as a project manager for the Ministry of Science and Technology’s Key Field Innovation Team and a technical expert for Chongqing Iron & Steel Electronic Co., Ltd. Dr. Fan has participated in over ten national R&D programs, published more than ten research papers, filed ten invention patents, and contributed to national and industrial standards. His groundbreaking work in data governance, business data modeling, and multi-value-chain distributed data spaces has benefited thousands of enterprises, earning him prestigious awards and international recognition.

 

Profile

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Education

Dr. Fan Bo has a strong academic foundation that underpins his expertise in industrial big data and digital transformation technologies. He earned his Ph.D. in a specialized field, equipping him with advanced knowledge and research skills crucial for tackling complex engineering and technological challenges. His academic journey reflects a commitment to excellence and a focus on integrating theoretical insights with practical applications, which has significantly contributed to his professional accomplishments in industrial innovation and data science.

 Experience

Dr. Fan Bo has a distinguished career marked by leadership and innovation in industrial big data and digital transformation. As Chief Technical Specialist at the National Engineering Laboratory for Industrial Big Data Application Technology, he oversees key technological advancements. He has participated in over ten national R&D programs, including leading a sub-project, and directed four key R&D initiatives in Sichuan Province. Additionally, Dr. Fan managed two horizontal digital transformation projects for China’s National Pipeline Group during the 13th and 14th Five-Year Plans. His concurrent roles as project manager for the Ministry of Science and Technology’s Key Field Innovation Team and technical expert for Chongqing Iron & Steel Electronic Co., Ltd. highlight his multifaceted expertise and ability to drive impactful projects in the field.

Research Interests

Dr. Fan Bo’s research interests focus on the application of industrial big data, data governance, and digital transformation technologies. He is particularly interested in the development and implementation of data governance platforms, business data models, and multi-value-chain distributed data spaces. His work aims to optimize industrial processes through innovative data solutions, particularly in the manufacturing and automotive sectors. Dr. Fan’s research also explores the integration of cloud service technologies and scenario-oriented business models to enhance efficiency and drive economic benefits for industrial enterprises. His contributions continue to shape the future of industrial data applications and digital innovation.

Skills

Dr. Fan Bo possesses a wide range of technical and leadership skills that have contributed to his success in industrial big data and digital transformation. He is highly skilled in data governance, designing and implementing complex data platforms, and developing business data models tailored to specific industrial scenarios. His expertise also extends to multi-value-chain distributed data spaces, enabling optimized data utilization across various industries, particularly in manufacturing and automotive sectors. Additionally, Dr. Fan has strong project management abilities, having led multiple high-impact initiatives under national R&D programs and major industrial projects. His proficiency in cloud service technologies, technical standard development, and innovation in digital transformation underscores his versatility and ability to drive technological advancements.

 

Publication

Title: Optimal Selection Technology of Business Data Resources for Multi-Value Chain Data Space—Optimizing Future Data Management Methods

  • Authors: Bo Fan, Linfu Sun, Dong Tan, Meng Pan
  • Journal: Electronics
  • Volume: 13
  • Issue: 23
  • Article Number: 4690
  • DOI: 10.3390/electronics13234690
  • Year: 2024

Conclusion

Dr. Fan Bo’s substantial research, innovation, and leadership in advancing industrial big data applications make him a deserving candidate for the Research for Best Researcher Award. His contributions not only address critical challenges in digital transformation but also generate tangible economic and industrial benefits, solidifying his position as a leading figure in his field.

Hui-Ling Yang | Inventory Control | Best Researcher Award

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Prof. Hui-Ling Yang | Inventory Control | Best Researcher Award

Scholar at HungKuang University, Taiwan

Prof. Hui-Ling Yang is a distinguished academic with extensive expertise in mathematics, statistics, and operations research. She holds a Ph.D. in Industrial Engineering from National Tsing Hua University, along with a Master’s and Bachelor’s degree in Mathematics from National Taiwan Normal University, Taiwan, ROC. Currently a Professor at HungKuang University, she has been a part of the institution since 1986, progressing through various roles. Her research focuses on inventory control, optimization strategies, and supply chain analytics. Prof. Yang has authored numerous journal articles and has been recognized in the Who’s Who in the World 25th Silver Anniversary Edition for her academic contributions.

 

Profile

Scopus

ORCID

Education

Prof. Hui-Ling Yang has a strong academic foundation, with a Bachelor of Science degree in Mathematics and a Master of Science degree in Graduate Mathematics from National Taiwan Normal University, Taiwan, ROC. She further advanced her studies by earning a Ph.D. in Industrial Engineering from National Tsing Hua University, Taiwan, ROC. Her diverse educational background equips her with interdisciplinary expertise, which she applies effectively in her teaching and research in mathematics, statistics, and operations research.

 Experience

Prof. Hui-Ling Yang has had a distinguished academic career spanning several decades at HungKuang University. She has served as a Professor since August 2004, following her tenure as an Associate Professor from February 2003 to July 2004. Prior to that, she held the position of Associate Professor at HungKuang Technology College from February 1998 to January 2003, and Lecturer from August 1997 to January 1998. Prof. Yang’s journey began as a Lecturer at HungKuang Junior College, where she contributed from August 1986 to July 1997. Her extensive teaching and research experience reflect her dedication to academia and her profound impact on her field.

Research Interests

Prof. Hui-Ling Yang’s research interests encompass a diverse range of topics in applied mathematics and operations research. Her expertise includes Mathematics, Statistics, and Operations Research, with a specialized focus on Inventory Control. She is particularly interested in developing optimal replenishment strategies and inventory models for deteriorating items, addressing fluctuating demand patterns, and exploring the impact of financial considerations such as advance cash-credit payment schemes and supplier credits. Her work integrates theoretical rigor with practical applications, offering innovative solutions to complex supply chain and inventory management challenges.

Skills

Prof. Hui-Ling Yang possesses a comprehensive skill set that spans Mathematics, Statistics, and Operations Research. Her expertise includes advanced analytical and problem-solving abilities, particularly in designing and optimizing inventory control models. She is proficient in formulating and solving complex mathematical models that address practical challenges in supply chain management. Additionally, Prof. Yang is skilled in conducting discounted cash-flow analyses, evaluating replenishment strategies, and integrating financial and operational decision-making. Her ability to bridge theoretical research with real-world applications highlights her strength in innovative and impactful academic contributions.

 

Publications

Optimal Replenishment Strategy for a High-Tech Product Demand with Non-Instantaneous Deterioration under an Advance-Cash-Credit Payment Scheme by a Discounted Cash-Flow Analysis

  • Authors: H.-L. Yang, C.-T. Chang, Y.-T. Tseng
  • Journal: Mathematics
  • Volume: 12 (19), Article: 3160
  • Year: 2024
  • Citations: 0

An Optimal Replenishment Cycle and Order Quantity Inventory Model for Deteriorating Items with Fluctuating Demand

  • Author: H.-L. Yang
  • Journal: Supply Chain Analytics
  • Volume: 3, Article: 100021
  • Year: 2023
  • Citations: 10

A Note on ‘A Two-Warehouse Partial Backlogging Inventory Model for Deteriorating Items with Permissible Delay in Payment under Inflation’

  • Author: H.-L. Yang
  • Journal: International Journal of Operational Research
  • Volume: 45 (2), Pages: 141–160
  • Year: 2022
  • Citations: 2

EOQ-Based Pricing and Customer Credit Decisions under General Supplier Payments

  • Authors: R. Li, H.-L. Yang, Y. Shi, J.-T. Teng, K.-K. Lai
  • Journal: European Journal of Operational Research
  • Volume: 289 (2), Pages: 652–665
  • Year: 2021
  • Citations: 25

Retailer’s Ordering Policy for Demand Depending on the Expiration Date with Limited Storage Capacity under Supplier Credits Linked to Order Quantity and Discounted Cash Flow

  • Author: H.-L. Yang
  • Journal: International Journal of Systems Science: Operations and Logistics
  • Volume: 8 (2), Pages: 136–153
  • Year: 2021
  • Citations: 6

An Optimal Ordering Policy for Deteriorating Items with Partial Backlogging and Time Varying Selling Price and Purchasing Cost under Inflation

  • Author: H.-L. Yang
  • Journal: International Journal of Operational Research
  • Volume: 31 (3), Pages: 403–419
  • Year: 2018
  • Citations: 5

An Inventory Model for Increasing Demand under Two Levels of Trade Credit Linked to Order Quantity

  • Authors: J.-T. Teng, H.-L. Yang, M.-S. Chern
  • Journal: Applied Mathematical Modelling
  • Volume: 37 (14-15), Pages: 7624–7632
  • Year: 2013
  • Citations: 75

A Two-Warehouse Partial Backlogging Inventory Model for Deteriorating Items with Permissible Delay in Payment under Inflation

  • Authors: H.-L. Yang, C.-T. Chang
  • Journal: Applied Mathematical Modelling
  • Volume: 37 (5), Pages: 2717–2726
  • Year: 2013
  • Citations: 90

Two-Warehouse Partial Backlogging Inventory Models with Three-Parameter Weibull Distribution Deterioration under Inflation

  • Author: H.-L. Yang
  • Journal: International Journal of Production Economics
  • Volume: 138 (1), Pages: 107–116
  • Year: 2012
  • Citations: 82

Conclusion

Prof. Hui-Ling Yang’s extensive contributions to research, coupled with her impactful publications and recognition, make her a strong candidate for the Research for Best Researcher Award. Her work bridges theoretical advancements and practical applications in inventory and operations management, aligning perfectly with the award’s criteria.