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MTH601 : Operations Research

Course Overview

Course Synopsis

This course presents the principles and techniques for solving Decision-making problems in the industry using Mathematical Models. It is the science of planning and executing operations to make the most economical use of resources available, also known as Management Science. The operation can be as simple as making a cup a tea as complicated as planning the movement of a fleet of tankers around the world. The techniques include Decision Analysis, Linear Programming, Transportation model, Assignment Model, Network Model, and Forecasting. The course emphasizes the process of model development and solution analysis.

Course Learning Outcomes

By the end of the course, students should be able:

  • To formulate a decision-making problem and apply the method of depicting a series of decisions and outcomes by decision trees.
  • To formulate the problems into mathematical model and apply the quantitative methods (graphical method and simplex method) for maximization and minimization problems
  • To formulate the transportation and assignment problems and apply the transportation simplex and Hungarian methods.
  • To define the concept of network analysis, develop the network diagram and perform network analysis.
  • To identify different types of forecast, to measure forecast accuracy and apply time-series forecasting models

Course Calendar

An overview1
OR Process2
OR Techniques and Applications3
Fundamental Concepts and Applications4
Construction of Networks5
Activities,Times and Floats6
To Find Critical Path7
To Find Critical Path8
CPM Model and PERT Model9
Resource Leveling10
Inventory Cost and Economic Order Quantities11
Purchasing Model and no Shortages12
Purchasing Model with Shortages13
Maunfacturing Model with no Shortages14
Manufacturing Model with Shortages15
Revision and Analysis16
Introduction to Linear Programming17
Formulation of LP Problems18
Formulation of LP Problems19
Solution of LP Problems20
Solution of LP Problems: Simplex Method21
Artificial Variables Techniques: Big M Method22
Big M Method:Examples23
Artificial Variables Techniques: Two Phase method24
Variant of Simplex Method25
Variant of Simplex Method: Degeneracy26
Variant of Simplex Method: Various Form of Solutions27
Variants of Simplex Method28
Duality Theory29
Post Optimality Analysis30
Transportation Models, Finding Initial Basic Feasible Solution31
Vogels approximation method32
Optimal Solution of Transportation Problems33
Examples on Transportation Problems34
Introduction to Assignment Problems and their Solutions36
Solution to Assignment Problem, Hungarian Algorithm37
Single Channel Infinite Population39
Replacment of Items with Gradual Deterioration40
Items Detoriating and Failing41
Basic Concepts and Development42
Solution of Dynamic Programming Problems43
Sequencing, Game Theory, Markov Chain, Integer Programming, Non Linear Programming44
Summing and Close Up45
Final Examination
Any kind of change in the schedule during semester will be announced on VULMS.
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