Syllabus

Module I [10 h]
Chapter 1. LPP: formulation, graphical, simplex, variations of LPP, big M, degeneracy, alternate optimal solution, Unbounded solution, infeasible solution, duality, dual LPP, dual simplex, sensitivity analysis, use of software
Chapter 2. Transportation Problem: formulation, as LPP, balanced, Corner methods, least cost method, Vogel’s Approximation method, check for optimality using u-v method, Modified distribution method, variations of transportation problem, Transshipment problem, use of software
Chapter 3. Assignment Problem: as LPP, as Transportation problem, Hungarian method, use of softwares
Module II [8 h]
Chapter 4. Network Models: Terminology, Shortest route, minimal spanning tree
Chapter 5. CPM & PERT: network construction, Fulkerson rule, ET/LT, critical path, floats (TF, FF, IF), Project Scheduling, EST/EFT, LST/LFT, AOA/AON, PERT, network crashing, CPM as LPP, use of software
Module III [8 h]
Chapter 6. Scheduling: Terms, types, assumptions, performance measures, routing, sequencing, Single processor scheduling: SPT, EDD, Moore, WSPT, FCFS, LCFS Flow shop scheduling (2/n, 3/n, m/n), Johnshon rule Job shop: EDD, SPT, FCFS, FISFS, LSF, LWR Aker
Chapter 7. Decision Theory: DMUU (pessimitic, optimistic, Hurwicz, Laplace, Regret) DMUC, DMUR (EMV, EOL, Decision tree, AHP)
Chapter 8. Game Theory: Pure strategies, dominated strategy, mixed strategy (Graphical, oddment, LPP)
Module IV [10 h]
Chapter 9. Queuing Theory: classification, Kendall’s nomenclature, Generalized Poisson queue, steady state probabilities, Little’s formula, Single server infinite/finite queue, Multi server infinite/finite queue,
Chapter 10. Simulation: classification, random number, pseudo random number, random numbers of different distributions, random number generation
Chapter 11. Forecasting: types of demands, classification of forecasting, Time series forecasting, Causal forecasting, Judgemental forecasting, forecast error

It introduces various quantitative techniques for decision making in the management of industrial organizations.

It teaches the skill of formulation of proper models suitable for operations management problems as well as the solution techniques in arriving at optimal decisions

It teaches the use of software for getting optimal solutions to various operations management problems

Upon completion of this laboratory course, the student will be able to
CO1: formulate mathematical models for various operations research problems like product mix problems, transportation problems, assignment problems etc.and solve them using various operation research methods including computer softwares.
CO2: solve various network type of operation research problems including project networks
CO3: prepare job scheduling for both single and multi-processor
CO4: solve decision theory and game theory problems
CO5: solve queuing theory problems and make forecast for sales demand

Taha, HA, Operations Research: An Introduction, 11e,, Pearson , 2023

Buffa, EW and Sarin, AK, Modern Production/ Operations Management, 8e, Wiley , 2021

Hillier, FS Lieberman, GJ Nag, B Basu, P, Introduction to Operations Research, 11e, McGraw Hill , 2021

Chary, SN, Production and Operations Management, 6e, McGraw Hill , 2023