Credits 3

Pre-requisites - None

Core/Elective - Core Course

To develop students’ analytical skills needed for applications in the areas of project management, industrial engineering and decision sciences so that they can utilize quantitative tools for the decision-making processes in engineering practice.

At the end of this course, students should be able to:

1. Formulate Linear, Integer and Mixed Integer Programming problems and use methods such as Simplex and branch and bound for their solution.

2. Use decision tables/ trees, and basic queueing models to make decisions under uncertainty, and risk.

3. Formulate Dynamic Programming models for standard problems (Knapsack, Shortest path search) and solve them using the tableau method.

4. Formulate multi criteria decision making problems using Data Envelopment Analysis; formulate and solve Analytic Hierarchy Problems (AHP) using matrix computations.

5. Identify different non-conventional, evolutionary and heuristic algorithms such as Genetic Algorithm, Simulated Annealing and Ant Colony Optimization.

6. Execute engineering projects as per project management lifecycle and use standard project management software for planning and management purposes.

1. Linear, Integer and Mixed Integer Programming Graphical solution method, Simplex method, Sensitivity analysis, Branch and Bound solution for Integer Programming problems

2. Dynamic Programming: Dynamic Programming in mathematical optimization (Knapsack and Shortest Path Search), Applications of Dynamic Programming

3. Decision Theory: Decision making under certainty, uncertainty, and risk

4. Introduction to Project Management: Projects and non-projects, project life cycle concept, project manager’s role, Nine knowledge areas of project management, Project planning and scheduling, Project selection Statement of Work (SOW), Work Breakdown-Structure (WBS) and Responsibility matrix, Network analysis techniques: Critical Path Method (CPM), Project Evaluation and Review, Technique (PERT), Gantt chart and resource mapping, Monitoring and controlling project cost, quality, and time Investment appraisal of projects, Computer applications for project management

5. Queueing Theory and Modelling: Single and multiple servers, infinite and finite source models

6. Introduction to Multi Criteria Decision Making: Introduction to Analytic Hierarchy Process (AHP), Data Envelopment Analysis (DEA)

7. Introduction to Non-Conventional Optimization Techniques: Evolutionary and heuristic algorithms