Credits 3

Pre-requisites - PR 315

Core/Elective - Technical Elective Course

To give students the knowledge and understanding of analytical and simulation methods used to measure/evaluate manufacturing system performance so that they can select appropriate methods to analyse real manufacturing systems.

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

1. Describe commonly used performance metrics of manufacturing systems and explain how they are measured/calculated.

2. Construct computer simulation models of manufacturing systems, evaluate their performance, and interpret the results.

3. Develop continuous time Markov chain models of small manufacturing systems and evaluate their steady state performance.

4. Use approximate methods to calculate the performance measures of larger manufacturing systems.

5. Develop queueing models of manufacturing systems and compute the average performance estimates using mean value analysis.

6. Compare and contrast the analytical and computer simulation methods of evaluating manufacturing system performance.

1. Introduction: Performance evaluation of manufacturing systems, Performance measures – definitions and relationships

2. Simulation of Manufacturing Systems: Manufacturing systems modelling, Generation of random variates Simulation procedure, Analysis of simulation input/output data Case studies

3. Markov Chain Models of Manufacturing Systems: Discrete Time Markov Chain (DTMC) models of small manufacturing systems, Continuous Time Markov Chain (CTMC) models of small manufacturing systems, Approximate methods for modeling larger manufacturing systems

4. Queueing Models of Manufacturing Systems: Single server models Open networks, Closed networks Mean value analysis