CE5972
Optimisation Techniques
Objectives:
- To develop fundamental understanding of different optimisation techniques applied in transportation engineering.
- To model complex transportation problems and solve them using suitable optimisation methods.
- To interpret solutions derived from optimisation models and make informed managerial decisions in real-world transportation scenarios.
Course Content:
- Linear programming: Principles of linear programming including the formulation of the objective function, decision variables, and constraints within the context of transportation engineering; graphical method and simplex algorithm; sensitivity analysis and duality; inferring results and making decisions.
- Non-linear programming: Principles of non-linear programming and associated solution methods including local search heuristics, evolutionary computation techniques, and swarm intelligence algorithms.
- Transportation Engineering problems: Variants of transportation problem, assignment problem, trans-shipment problem, least-cost path problem, traveling salesman problem, vehicle routing problem, facility location problem, and network design problem.
Textbooks:
- Hillier, F. S., & Lieberman, G. J. (2024). Introduction to operations research. (12th edition). McGraw-Hill.
- Winston, W. L. (2022). Operations Research: Applications and Algorithms. (4th edition). Cengage Learning.
- Al-Khateeb, G. (2020). Solved Practical Problems in Transportation Engineering. (1st edition). Taylor & Francis Group.
Reference Books:
- Taha, H. A. (2022). Operations Research: An Introduction. (11th edition). Pearson Education India.
- Balakrishnan, N., Render, B., & Stair, R. M. (2013). Managerial decision modelling with spreadsheets. (3rd edition). Prentice Hall Press.
Lectures:
| SNo. | Topic |
|---|---|
| 01 | Introduction |
| 02 | Five Step Process |
| 03 | Problem Types |
| 04 | Linear Programming |
| 05 | Graphical Solution Method |
| 06 | Graphical Solution Method |
| 07 | Assignment #1 Discussion |
| 08 | Spreadsheet-based Solution Method |
| 09 | Basic Linear Algebra |
| 10 | Simplex Method |
| 11 | Sensitivity Analysis |
| 12 | Duality |
| 13 | Assignment #2 Discussion |
| - | Quiz-I |
| 14 | Quiz-I Discussion |
| 15 | Python Programming |
| 16 | Transportation Problem |
| 17 | Transshipment Problem |
| 18 | Minimum Spanning Tree Problem |
| 19 | Maximum Flow Problem |
| 20 | Assignment #3 Discussion |
| 21 | Least-Cost Path Problem |
| 22 | Travelling Salesman Problem |
| 23 | Vehicle Routing Problem |
| 24 | Location Routing Problem |
| 25 | Assignment #4 Discussion |
| - | Quiz-II |
| 26 | Quiz-II Discussion |
| 27 | Non-Linear Programming |
| 28 | Metaheuristics |
| 29 | Hill Climb |
| 31 | Threshold Accepting |
| 32 | Simulated Annealing |
| 33 | Tabu Search |
| 34 | Iterative Local Search |
| 35 | Variable Neighbourhood Search |
| 36 | Evolutionary Computation |
| 37 | Swarm Intelligence |
| 38 | Case Study |
| 39 | Case Study |
| 40 | Assignment #5 Discussion |
| 41 | Surplus Lecture |
| 42 | Surplus Lecture |
| - | End Sem |