Professor Hoong Chuin Lau
Singapore Management University

Planning and Scheduling in Logistics and Supply Chains – Theory and Applications

Planning and Scheduling are optimisation problems which require finding a set of actions to complete a task, to achieve a goal, or to optimise one or more objectives. Effective solutions to planning and scheduling problems are critical for a variety of important application areas, including Industry 4.0, aerospace systems, logistics and supply chain management, robotics, education, healthcare and more.

This short course is concerned with planning and scheduling problems arising in transportation, logistics and supply chains. Drawing from AI and Operations Research, we will discuss the theory and applications of intelligent and automated planning and scheduling technology. Beyond a standard optimization course that focusses on methods and stylized problems, we take a business-oriented approach where we discuss contemporary challenges in these problems, such as resilience, environmental sustainability, crowdsourcing, and other real-world issues.

Course objectives

After the successful completion of this course, students should be able to:

  1. Acquire basic skills in AI and Operations Research for defining and modeling optimization problems in Transportation, Logistics and Supply Chains
  2. Design efficient algorithms to solve these problems and implement them in Python
  3. Learn various Metaheuristics techniques such as Local Search, Simulated Annealing, Large Neighborhood Search, Particle Swarm, and Genetic Algorithms
  4. Learn how to handle Planning and Scheduling problems under uncertainty, by formulating them as Stochastic Optimization and Robust Optimization models and solving them using Sample Average Approximation
  5. Learn how to address dynamic Planning and Scheduling problems, by formulating them as Markov Decision Process and solving them using Reinforcement Learning
  6. Apply foundational methods to problems in Supply Chains
  7. Apply foundational methods to problems Transportation and Logistics
  8. Learn how to model optimization problems to be solved on Quantum simulators or hardware

Topics

  • Metaheuristics Part 1
    • Meta-heuristics in the context of Optimization techniques
    • Local Search and Simulated Annealing
    • Tabu Search
  • Metaheuristics Part 2
    • Large Neighborhood Search and ALNS
    • Evolutionary Approaches and Swarm Intelligence
    • Applications in Logistics
  • Optimisation under Uncertainty Part 1
    • Stochastic Optimization and Recourse Models
    • Contrast with Robust Optimization
    • Solving Stochastic Optimization with Sample Average Approximation
  • Optimisation under Uncertainty Part 2
    • Application 1: supply chain resilience (project with IBM)
    • Application 2: coordinated deliveries (project with Singapore logistics companies)
  • Dynamic Optimization Part 1
    • From Static to Dynamic Planning and Scheduling
    • Introducing Markov Decision Processes and Reinforcement Learning
    • Combining RL with Metaheuristics
  • Dynamic Optimization Part 2
    • Application 1: Urban Last-Mile Delivery (project with Parcel Delivery operator)
    • Application 2: Urban Patrol Scheduling (project with Singapore Police Force)
  • Data-Driven Optimization Part 1
    • AI meets OR revisited
    • Application 1: Fuel-Saving Tugboat Scheduling (project with Singapore Maritime Port Authority)
  • Data-Driven Optimization Part 2
    • Application 2: Driver-Centric same-day parcel delivery (project with logistics company)
    • Application 3: GRAND-VISION
  • Quantum Optimisation Part 1
    • Computational Complexity of Optimization Problems
    • Formulating Optimization Problems as QUBO
    • Solving QUBOs on Quantum Computers (Quantum Annealers, QAOA/VQE and their variants)
  • Quantum Optimisation Part 2
    • How to Implement on Quantum Hardware
    • Implementation Challenges

Pre-Reading

Recommended Textbooks:

Professor Hoong Chuin Lau

Professor Hoong Chuin Lau
Singapore Management University

Hoong Chuin LAU is a Professor of Computer Science at the School of Computing and Information Systems, Singapore Management University. He also holds a joint appointment as Senior Principal Scientist at the Institute of High Performance Computing under the Singapore Agency for Science, Technology and Research (A*STAR). From 2019 to 2022, he also held the position of Specially Appointed Professor of AI and Information at the Tokyo Institute of Technology, Japan.

Hoong Chuin has over a decade of experience leading nationally funded research labs. He served as Director of the Fujitsu-SMU Urban Computing and Engineering Corporate Lab from 2014 to 2021 and was Deputy Director of the Living Analytics Research Lab from 2011 to 2015. Throughout his career, he has secured more than SGD 30 million in grants from both government and industry sources. Over the past five years, Hoong Chuin has been the Principal Investigator (PI) for industry projects funded by IBM, the Ministry of Home Affairs, Fujitsu Labs (Japan), and three AI Singapore “100 Experiments for Research” (100E4R) projects. These projects involved extensive field trials in collaboration with prominent industry partners, including the IBM Manufacturing Solutions, Singapore Police Force, Tan Tock Seng Hospital, the Maritime and Port Authority of Singapore, Keppel Logistics, and Jurong Port. He is an accomplished scholar who works at the interface of AI and Operations Research. His work bridges the gap between academia and industry, focusing on the development of computationally efficient, data-driven models to address complex planning and scheduling challenges in areas such as transportation, logistics, supply chains, healthcare, and emergency operations. One notable achievement is his development of the Collaborative Urban Delivery Optimization (CUDO) technology, recognized as one of AI Singapore’s featured products (https://aisingapore.org/aiproducts/cudo/). CUDO has been patented and successfully licensed for industrial application.

Hoong Chuin was the appointed chair of the AI Singapore’s Grand Challenge Program Committee in Urban Solutions. Since 2020, he has served on the selection committee for the prestigious President’s Science and Technology Awards, along with other national committees, conference organizing and program committees (AAAI/IAAI, IJCAI, ICAPS, AAMAS). He serves on a number of editorial boards, including ACM Journal on Autonomous Transportation Systems. Journal of Scheduling, Journal of Heuristics and IEEE Transactions on Automation Science and Engineering.

In a global study by Stanford University and Elsevier published since 2020, he has been ranked among the top 2% of scientists globally in the field of Artificial Intelligence. In recognition of his significant contributions to academia and industry, Hoong Chuin was honored with the Outstanding Professor Award by the Industrial Engineering and Operations Management Society in 2021.

In terms of teaching, Hoong Chuin has taught a wide range of undergraduate and graduate courses, including AI Planning and Decision Making, Design and Analysis of Algorithms, Combinatorial Graph Algorithms, Computational Thinking, Decision Analytics and Optimization, Advanced Topics in Intelligent Systems, and Enterprise Analytics for Decision Support. He is also the co-author of the textbook “Business Analytics for Decision Making”, published by CRC Press in 2016.

Twice a recipient of Singapore government scholarship from the Infocomm Development Authority (IDA), Hoong Chuin obtained his Doctorate of Engineering degree in Computer Science from the Tokyo Institute of Technology (Japan) in 1996, and BSc and MSc degrees in Computer Science from the University of Minnesota (Minneapolis, USA) in 1987 and 1988.