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Course Detail

Course Name Operations Research
Course Code 19CCE332
Program B. Tech. in Computer and Communication Engineering
Year Taught 2019


Unit 1

Evolution of OR – Fundamentals of OR Modelling Approach – Linear Programming –Assumptions – formulation -graphical method -simplex method – dualitytheory- primal-dual relationships -sensitivity analysis Transportation and Assignment Problems – Specific features and methods of transportation problem -Hungarian method for solving assignment problems – travelling salesman problem – Dynamic Programming – Characteristics – optimality principle -deterministic problems.

Unit 2

Network Models- Project Networks- CPM / PERT- Project Scheduling – crashing networks and cost considerations – Resource leveling and smoothing, shortest route problem – minimal spanning tree problem – maximal flow problem Decision Theory – Decision making under uncertainty – decision trees – decision under risk – EMV, EOL, EVPI – Game theory – mixed strategies – dominance property – 2 x n and m x 2 games.

Unit 3

Flow shop scheduling– Johnsons algorithm for n jobs and two machines and n jobs and m machines – Inventory Models – Deterministic manufacturing and purchase models – quantity discounts Queueing models – Poisson arrival and exponential service times – Single server and multi-server modelSimulation –Monte Carlo simulation – simple problems.


  • Taha, H A, “Operations Research – An Introduction”, Sixth Edition, Prentice Hall of India Private Limited, N. Delhi, 2004.
  • Ravindran, A., Phillips, D.J., and Solberg, J.J., “Operations Research- Principles and Practice”, John Wiley & Sons, 2005.


  • Wagner H M, “Principles of Operations Research”, Second Edition, Prentice Hall of India Private Limited, New Delhi, 2003.
  • Hardley, G., “Linear Programming”, Narosa Book Distributors Private Ltd 2002.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

Objectives and Outcomes


  • To familiarize students with quantitative techniques applied in business decision making scenarios
  • To comprehend the mathematical methods to optimize the company resources and find optimal solution within constraints

Course Outcomes

  • CO1: Identify, formulate and analyze models to optimize organizational resources and maximize profit
  • CO2: Choose the appropriate management tool to lead a team and provide solutions in complex decision making Scenarios
  • CO3: Apply continuous learning and help improve company financials by efficiently managing projects
  • CO4: Analyse the data and comprehend on performance based on scheduling, queueing and inventory

CO – PO Mapping

CO1 3 3 3 3 2 3 1
CO2 3 1 3 3 3 2 3 1
CO3 3 3 3 3 2 1 3 3 1
CO4 1 3 3 3 1 3 2

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