lobal Logistics and Supply Chain Management Laboratory
Research Field
Principal Investigator: Vincent F. Yu, Ph.D.
Professor Vincent F. Yu is a distinguished scholar in operations research, logistics and supply chain management, and computational intelligence, and serves as a full professor in the Department of Industrial Management at National Taiwan University of Science and Technology (Taiwan Tech). He is also the Director of the Global Logistics & Supply Chain Management Laboratory and holds a concurrent professorship in the Graduate Institute of Intelligent Manufacturing Technology.
Professor Yu earned his M.S. and Ph.D. in Industrial & Operations Engineering from the University of Michigan, Ann Arbor, complemented by an M.S. in Mathematics from Michigan State University and a B.S. in Mathematics from National Taiwan Normal University.
His research portfolio spans the development and application of quantitative decision models, optimization techniques, and intelligent systems to address complex operational challenges in logistics, supply chains, transportation systems, and energy system optimization. His core research areas include network optimization, vehicle routing and distribution planning, logistics network design, soft computing and metaheuristics, data analytics and machine learning, and sustainable and smart logistics systems.
Professor Yu has authored over 100 peer-reviewed journal articles and more than 200 conference papers, contributing significantly to the scientific literature in his fields of expertise. His work has been recognized with multiple national and international awards, including institutional research excellence awards and the Outstanding Researcher Award from the Industrial Engineering and Operations Management (IEOM) Society International. He is also consistently listed among the world’s top 2% scientists.
In addition to his research contributions, Professor Yu plays an active role in the professional community as editorial board member for international journals and leader in professional societies. His research has been translated into industry collaborations with global logistics service providers and technology firms, reflecting a strong commitment to bridging academic innovation and practical impact.
Global Logistics & Supply Chain Management Laboratory
The Global Logistics & Supply Chain Management Laboratory, directed by Professor Vincent F. Yu at the Department of Industrial Management, National Taiwan University of Science and Technology, focuses on advancing research in logistics, supply chain systems, and operations optimization. The laboratory serves as an interdisciplinary research environment integrating theory, computation, and empirical applications to address complex decision problems in global logistics and supply chain management.
Mission and Research Scope
The primary objective of the laboratory is to develop innovative quantitative models, algorithmic methods, and decision support systems that improve the performance, sustainability, and resilience of logistics and supply chain networks. Emphasis is placed on bridging foundational research in operations research, optimization, and computational intelligence with practice-oriented problems in transportation, distribution, and service systems.
Core Research Themes
- Logistics Network Design and Planning: Network structure optimization, Integration of forward and reverse logistics flows
- Vehicle Routing and Transportation Optimization: Development of exact and heuristic solution methods for variants of the vehicle routing problem (e.g., electric vehicles, multi-echelon distribution, cross-docking).
- Sustainable and Smart Logistics: Optimization models incorporating environmental objectives (e.g., emissions reduction), Integration of public transportation for last-mile logistics.
- Computational Intelligence and Metaheuristics: Application of fuzzy logic, metaheuristics, and hybrid optimization frameworks to complex combinatorial problems.
- Decision Analytics and AI Integration: Data-driven analysis, machine learning methods, and intelligent decision support for logistics and operational systems.
Educational and Collaborative Environment
The laboratory trains graduate and undergraduate students in both theoretical modeling and practical solution techniques. Research outcomes frequently span peer-reviewed publications, collaborative projects with industry partners, and real-world case studies addressing logistics and supply chain challenges.
Location and Structure
The lab is housed in room MA-003 within the College of Management facilities, serving as a hub for research activities, seminars, and student mentoring under Professor Yu’s supervision.
Professor Vincent F. Yu’s research spans a broad range of topics in operations research, logistics, optimization, and intelligent systems. His principal research interests can be summarized as follows:
Core Research Areas
- Operations Research — Development and application of quantitative decision-making models including network flows, integer programming, large-scale optimization, and multi-criteria decision making (MCDM).
- Logistics and Supply Chain Management — Optimization of logistics networks, supply chain design, city logistics, green logistics, production scheduling, and related planning problems.
- Vehicle Routing and Transportation Optimization — Design and solution of vehicle routing problems (including electric vehicle routing, multi-depot routing, two-echelon routing, reverse logistics and cross-docking variants) with exact, heuristic, and metaheuristic methods.
- Soft Computing and Metaheuristics — Application of fuzzy logic, metaheuristic and matheuristic algorithms (e.g., simulated annealing, adaptive large neighborhood search) to complex combinatorial optimization problems.
- Artificial Intelligence and Data Analytics — Integration of AI, machine learning, and data-driven analytics into decision support systems, smart logistics and intelligent operations.
- Energy System Optimization — Optimization under uncertainty for energy networks, including maintenance scheduling, renewable integration, smart grids, and related policy modeling.
Application Domains
- Sustainable and green supply chains, focusing on minimization of environmental impacts while optimizing performance.
- City logistics and last-mile delivery systems, including integration of public transportation and parcel lockers.
- Smart transportation services such as demand-responsive transit and innovative routing models.
Overall, his work bridges methodological advances in optimization and computational intelligence with practical logistics and supply chain problems relevant to both industry and public infrastructure.
World's Top 2% Scientist (2021, 2023, 2024)
Excellent Research Award (NTUST), five times
Outstanding Research Award (NTUST)
Outstanding Researcher Award from the Industrial Engineering and Operations Management (IEOM) Society International
PhD in Industrial & Operations Engineering, University of Michigan, Ann Arbor
Job Description
The Research Intern will conduct research on logistics optimization and intelligent transportation systems, with a primary focus on artificial intelligence and reinforcement learning (RL) for complex and dynamic decision-making problems. The work emphasizes learning-based and hybrid AI–optimization frameworks for large-scale logistics systems.
Specific tasks may include:
- Developing reinforcement learning or learning-based optimization models for logistics and transportation problems (e.g., routing, fleet management, and dynamic resource allocation).
- Integrating RL with classical optimization methods, heuristics, or metaheuristics to improve solution quality and computational efficiency.
- Building and evaluating simulation environments for training and testing learning-based policies under uncertainty and time-dependent conditions.
- Benchmarking AI-based approaches against traditional optimization methods.
- Assisting with research documentation, technical reports, and preparation of conference and journal manuscripts.
This position is intended for students seeking long-term research collaboration, with preference given to those planning to pursue Master’s or PhD studies in AI, data-driven optimization, or related areas.
Preferred Intern Educational Level
Senior undergraduate, Master’s, or PhD student in:
- Industrial Engineering
- Transportation Engineering
- Computer Science
- Artificial Intelligence
- Data Science
- Operations Research or related disciplines
A clear interest in logistics systems or intelligent transportation research is strongly preferred.
Skill sets or Qualities
Applicants are expected to demonstrate strong technical foundations, analytical ability, and motivation for research in logistics optimization and intelligent transportation systems. In particular, the following skills and qualifications are desirable:
- Proficiency in Python programming (C++ is a plus).
- Fundamental understanding of machine learning and reinforcement learning.
- Background in operations research (e.g., linear programming, integer programming, stochastic programming), logistics optimization or transportation modeling (e.g., VRP, scheduling, network flows) is highly desirable.
- Familiarity with one or more of the following is an advantage:
- PyTorch or TensorFlow
- Optimization solvers (e.g., Gurobi, CPLEX)
- Metaheuristics and hybrid optimization methods
- Strong analytical thinking and problem-solving ability.
- Ability to read, understand, and implement algorithms from academic literature.
- Good academic writing and communication skills in English.
- Self-motivated, detail-oriented, and capable of independent research.
Application Requirements:
Please submit the following materials by email:
- Curriculum Vitae (CV)
- Brief statement of research interests related to logistics or intelligent transportation (≤ 1 page)
- (Optional but encouraged) GitHub repository or code portfolio
- Intended starting date and expected duration of the internship
Job Description
The Research Intern will conduct research on logistics optimization and intelligent transportation systems, with a primary focus on operations research and mathematical programming. The work emphasizes rigorous problem formulation and the development of exact and hybrid solution methods for large-scale logistics and transportation problems.
Specific tasks may include:
- Formulating linear, integer, and mixed-integer programming (LP/ILP/MILP) models for logistics and transportation problems such as vehicle routing, scheduling, and network design.
- Developing exact algorithms, decomposition methods, or matheuristics for solving large-scale optimization problems.
- Designing computational experiments to evaluate solution quality, scalability, and robustness.
- Comparing mathematical programming approaches with heuristic and metaheuristic methods.
- Assisting with research documentation, technical reports, and preparation of conference and journal manuscripts.
This position is intended for students seeking long-term research collaboration, with preference given to those planning to pursue Master’s or PhD studies in optimization-related fields.
Preferred Intern Educational Level
Senior undergraduate, Master’s, or PhD student in:
- Industrial Engineering
- Transportation Engineering
- Computer Science
- Artificial Intelligence
- Data Science
- Operations Research or related disciplines
A clear interest in logistics systems or intelligent transportation research is strongly preferred.
Skill sets or Qualities
Applicants are expected to demonstrate strong technical foundations, analytical ability, and motivation for research in logistics optimization and intelligent transportation systems. In particular, the following skills and qualifications are desirable:
- Proficiency in Python programming (C++ is a plus).
- Fundamental understanding of machine learning and reinforcement learning.
- Background in operations research (e.g., linear programming, integer programming, stochastic programming), logistics optimization or transportation modeling (e.g., VRP, scheduling, network flows) is highly desirable.
- Familiarity with one or more of the following is an advantage:
- PyTorch or TensorFlow
- Optimization solvers (e.g., Gurobi, CPLEX)
- Metaheuristics and hybrid optimization methods
- Strong analytical thinking and problem-solving ability.
- Ability to read, understand, and implement algorithms from academic literature.
- Good academic writing and communication skills in English.
- Self-motivated, detail-oriented, and capable of independent research.
Application Requirements:
Please submit the following materials by email:
- Curriculum Vitae (CV)
- Brief statement of research interests related to logistics or intelligent transportation (≤ 1 page)
- (Optional but encouraged) GitHub repository or code portfolio
- Intended starting date and expected duration of the internship