Module I:
Optimization Problem Formulation. Introduction to Linear Programming Formulations. Linear Programming Solutions: Graphical Methods, Simplex Methods. [6 hrs.]
Module II:
Simplex Algorithm: Minimization Problems, Primal Dual Relationships, Duality Theorems. Traditional Optimization Techniques: Lagrangean, Kuhn-Tucker. Linear Programming. Integer Programming. Marginal Analysis. Sensitivity Analysis. [6 hrs.]
Module III:
Non-Traditional Optimization Technique: Genetic Algorithm. Decision Analysis: Introduction to Probability, Decision under Certainty, Decision under Risk, Decision under Uncertainty, Game Theory. [6 hrs.]
Module IV:
Markov Chains, Queuing Systems, Project Scheduling: Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM), Time Chart and Resource Leveling. [6 hrs.]
Module V:
Parameter Estimation. Hypothesis Testing. Regression Analysis. [6 hrs.]
Module VI:
Network Analysis: Travelling Salesman Problem Shortest Path: Moore’s Algorithm, Dijkastra’s Algorithm, Floyd’s Algorithm. [6 hrs.]

Civil Engineering operation

Planning and scheduling methodologies to complicated Civil Engineering problems

System operation and improvement

Routing methodologies to networks

After completion of the course, students will be able to:
CO1: Solve emergent engineering problems in real life.
CO2: Formulate Linear and Non-Linear Optimization Problems.
CO3: Evaluate Non-Traditional Optimization Techniques.
CO4: Analyze Transportation Networks.
CO5: Conduct analyses related to sustainable Civil Engineering development.

H.A. Taha, Operations Research, Prentice Hall of India

F. S. Hillier and G. J. Lieberman, Introduction to Operations Research, Tata McGraw-Hill

D.N. Gujarati, Basic Econometrics, Tata McGraw-Hill

K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons