Smart Logistics

CE 56901

Credit Hours:

3

Instructor:

Professor Satish V. Ukkusuri

Learning Objectives:

  1. Reinforce the integrated nature of logistics systems from tactical, operational and strategic perspectives, different actors in this system and the role of logistics systems as an economic driver.
  2. Understand the basics concepts and models of demand prediction, inventory management, operational and tactical planning in supply chain management.
  3. Demonstrate the ability to develop appropriate quantitative tools for planning and logistics problems using optimization techniques and solve them using appropriate solution algorithms, techniques and software.
  4. Apply the science of logistics systems to improve the cost and overall efficiency of real world logistics problems.

Description:

This course provides a foundation of analytical tools, methods and applications of logistics systems in the context of planning and operations of integrated supply chain systems.  The material is useful for students interested in managing supply chain systems providing a background on where and how specific methods can be used for improving overall performance of the supply chain. The course is broadly divided into two parts: (1) Science of Logistics which provides an introduction to unique characteristics of supply chain management; demand forecasting, planning and management; inventory control and planning; operational transportation issues such as vehicle routing and supply chain contracts and network design. (2) Business of Logistics which discusses the applications of the science to real-world logistics systems.  Real world case studies from past problems will be the basis for discussion and will include the nature of costs in supply chain networks, operational issues, vehicle routing problems, interactions of carriers and shippers using auctions and yield management. The course will use intuitive arguments and mathematical optimization tools will be used to illustrate many situations in a rigorous fashion.

Topics Covered:

Conceptual Foundation, Tactical level: Demand forecasting and inventory control, Operational level: Operational networks and shipper perspective, Strategic level: Network design and supply chain contracts.

Prerequisites:

Undergraduate calculus, basic knowledge of probability and statistics at the undergraduate level. Competency in using excel and VB for data analysis. As a graduate elective, this course is appropriate for students with an interest in learning about models and business aspects of logistics systems.

Applied / Theory:

Demand forecasting method, Inventory control method, Operational networks, Routing & Scheduling algorithm, Yield management, Supply chain contracts 

Web Address:

https://purdue.brightspace.com

Web Content:

Syllabus, grades, lecture notes, homework assignments, solutions, quizzes, exam, and project

Homework:

  • Three problem sets will be given, and the analysis of these assignments will be the basis for some class discussion.
  • Problem sets are due at the beginning of class on designated days; late problem sets will not be accepted..

Projects:

 

Exams:

There will be one in-class (virtual) examination in which you will be tested on readings and materials/discussions covered in class. 

Textbooks:

Texts:

  • No recommended text book. The material will be derived from various sources which will be distributed by the instructor
  • (LL): Simchi-Levi, David; Chen, Xin; and Bramel, Julien. The Logic of Logistics, 2nd edition, Springer, 2005.
  • (UOR): Chapter 6. Applications of Network Models. Urban Operations Research. Larsen and Odoni.
    http://web.mit.edu/urban_or_book/www/book/chapter6/contents6.html

Other References:

  • Ahuja, R.K., Magnanti, T.L. and Orlin, J.B. Network Flows: Theory, Algorithms and Applications. Prentice-Hall Inc., 1993.
  • Daganzo, Carlos, Logistics Systems Analysis, Fourth Edition, Springer, 2005.

Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.

Tentative Textbook Listing:

N/A

Computer Requirements:

Python (interpreted high-level programming language for general-purpose programming) and its libraries for data mining (numpy, scipy, matplotlib, etc.). Available for free download at https://www.python.org/.