CE 5972 - Optimisation Techniques in Transportation Engineering

CE 5972 - Optimisation Techniques in Transportation Engineering#

To model complex transportation problems and solve them using suitable optimisation methods
To develop fundamental understanding of different optimisation techniques applied in transportation engineering
To interpret solutions derived from optimisation models and make informed managerial decisions in real-world transportation scenarios

Module 1. Linear Programming
- Principles of linear programming: objective function, decision variables, and constraints
- Solution Method: graphical method and simplex algorithm
- Decision-Making: inferring results, sensitivity analysis, and duality
Module 2. Transportation Engineering Problems
- Fundamental Problems: transportation problem, assignment problem, trans-shipment problem
- Routing Problems: least-cost path problem, traveling salesman problem, vehicle routing problem, facility location problem
- Other Problems: Network Design Problem
Module 3. Non-linear Programming
- Principles of non-linear programming
- Solution Method: local search, evolutionary computation, and swarm intelligence metaheuristics
- Case Studies

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

S. No.	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

S. No.	Content	Marks	Due Date
1	Lectures 02 - 06	30	Lecture 7
2	Lectures 08 - 12	30	Lecture 13
3	Lectures 15 - 19	25	Lecture 20
4	Lectures 21 - 24	25	Lecture 25
5	Lectures 27 - 38	25	Lecture 39

S. No.	Content	Marks
1	Lectures 02 - 06, 08 - 12	30
2	Lectures 15 - 19, 21 - 24	30