3 sessions / week, 2 hours / session.
This course is an introduction to the fundamentals of logistics systems. The topics are introduced from an engineering perspective. Emphasis is given to theoretical foundations, mathematical tools, modeling, heuristic notions and algorithms.
The course is organised in three main topic areas.
Demand forecasting (2 lectures)
Inventory management (4 lectures)
Transportation and distribution planning and operations (2 lectures)
We will make use of numerical examples and MATLAB coding to illustrate the concepts in practice.
Class will be a mix of lectures and tutorials.
At the end of the course the student is able to:
Choose and implement the most appropriate models and methods to address logistics problems
Analyse the performance measures and optimally design the inputs under deterministic or stochastic, known and unknown parameters in supply chains and perform numerical analysis and design of small-scale examples on paper
Develop a code using computational language (MATLAB) to perform numerical analysis and design on large-scale examples in logistics and supply chains
Final Exam (60%). A final exam at the end of the course will cover all topic areas. The class textbook and a reasonable amount of lecture notes will be allowed.
Individual Project (40%). Each student will need to develop a small project involving the following steps.
identification of a sensible logistics problem (to evaluate the student's creativity);
development of a model (to evaluate the student's scientific thinking)
conduct of theoretical analysis and design (to evaluate the student's analytical insight)
development of numerical analysis (to evaluate the student's coding skill)
Interpretation of the findings (to evaluate the student's technical writing)
Class participation. Students are encouraged to show proactive participation in class to discussions and exercises during frontal lessons and tutorials.
Week 1
Lecture 1: Introduction, Forecasting methods, Time-series models, Components of demand (mean, trend, seasonality, randomness), Moving average, Exponential smoothing. Tutorial 1 Template Report Practice a bit with these Exercises
and here are the Solutions
Lecture 2: Forecasting, Trend Models, Linear Regression and Error Sum of Squares, Holt's model and Exponential Smoothing, Winters Model and Seasonality. Tutorial 2 Assignment 2 Solutions to Assignment 2
Week 2
Lecture 3: Inventory Management, Economic order quantity (EOQ), Sensitivity analysis. All units quantity discount, Finite Replenishment Rate: The Economic Production Quantity (EPQ). Tutorial 3
Lecture 4: Style goods and perishable items, Newsvendor boy problem. Tutorial 4
Week 3
Lecture 5: Dynamic economic lot sizing, Simple heuristics (Three-month rule, Use of a Fixed EOQ), Exact methods (Wagner-Whitin method), Ad-hoc heuristic (The Silver-Meal heuristic). Tutorial 5
Week 4
Lecture 6: Stochastic lead time demand, Issues and terminology, Order point, order quantity (s,Q) policy, An Example Using a Discrete Distribution, Common derivation. Tutorial 6
Week 5
Lecture 7: Combinatorial optimization, Linear programming relaxation, Knapsack and applications (Capital budgeting, Multi-period capital budgeting), Bin Packing and applications (Capacitated vehicle routing problem), Production mix with setup costs, Transportation with setup costs. Tutorial 7
Week 6
Lecture 8: Scheduling, Traveling salesman problem, Set covering, packing and partitioning, Examples of Facility Locations and Distribution Route Planning. Tutorial 8
Textbook
Silver, Pyke and Thomas, Inventory and Production Management, CRC Press, 2016, Fourth Edition ISBN 9781466558618.
Additional material
Operations and supply chain management, Lecture 2, Prof G Srinivasan, Department of Management studies, Indian Institute of Technology, https://www.youtube.com/watch?v=k9dhcfIyOFc
Martello, Toth, Knapsack problems, Chapters 1,2, and 8, John Wiley & Sons.
Hillier, Lieberman Introduction to Operations Research, McGraw-Hill Higher Education; 10th edition edition (June 16, 2014)