302– Decision Science University Syllabus 2023

 

302– Decision Science University Syllabus 2023

1. Introduction: Importance of Decision Sciences & role of quantitative techniques in decision making. Assignment Models: Concept, Flood’s Technique/ Hungarian method, applications including restricted, multiple assignments and maximization objective. Transportation Models: Concept, formulation, problem types: balanced, unbalanced, restriction and maximization, Basic initial solution using North West Corner, Least Cost & VAM, Optimal solution using MODI, multiple solution case to be considered. (8+2) 

 2. Linear Programming: Concept, Formulation & Graphical Solution. Markov Chains: Applications related to management functional areas, estimation of transition probabilities. Simulation Techniques: Monte Carlo Simulation, scope, and limitations. (7+2) 

 3. Probability: Concept, Conditional Probability theorem-based decision making. Probability Distributions: Normal, Binomial, Poisson (Simple numerical for decision making expected). Queuing Theory: Concept, Single Server (M/M/I, Infinite, FIFO), Introduction of Multi Server (M/M/C, Infinite, FIFO) (Numerical on single server model expected) (8+2) 

 4. CPM & PERT: Concept, Drawing network, identifying critical path, Network calculations- calculating EST, LST, EFT, LFT, Slack, floats & probability of project completion in case of PERT. Network crashing: Concept of project cost and its components, time and cost relationship, crashing of CPM network. (8+2) 

 5. Decision Theory: Concept, Decision making under uncertainty Maximax, Maximin, Minimax regret, Hurwicz’s & Laplace criterion, Decision making under risk (EMV, EVPI) for items with and without salvage value. Game Theory: Concept, 2 × 2 zero sum game, Pure & Mixed Strategy, solution of games with dominance, average dominance method. Sequencing problem: Introduction, Problems involving n jobs-2 machines, n jobs- 3 machines & n jobs-m machines, Comparison of priority sequencing rules.(6+2)