Introduction Goal: Understand how OR contributes in solving some industrial problems characterized by the stochastic conditions Outcome: Enable to model some stochastic situations Enable to solve some stochastic model Understand the basic of simulation
Why concern on stochastic? Some facts: Shop-floor condition is seldom stable for more than half an hour (McKay et al, 1995) We can never predict demand for a product accurately Although preventive maintenance is done, we can never eliminate the occurrence of machine breakdown Due to the process variability, the processing time is seldom constant etc The only certainty in the real world is uncertainty
Course outline Inventory models Queuing theory Introduction to stochastic process Continuous Time Markov Chain Discrete Time Markov Chain Exponential distribution and poison process Introduction to simulation Generating random number and random variates
References P.A. Jensen and J.F. Bard, 2003, Operations Research: Models and Methods, John Wiley & Sons, New York. F.S. Hiller and M.S. Hiller, 2004, Introduction to Management Science, McGraw-Hill Book Co., Boston. Ross, S.M. Stochastic Processes, 2 nd ed., John Wiley and Sons, Inc., Canada J. Banks, J. S. Carson, II, B. L. Nelson, 2000, Discrete-Event System Simulation. Upper Saddle River, NJ : Prentice Hall, 3rd ed. V. David, 1996, Quantitative Risk Analysis: Monte Carlo Approach, John Willey & Sons, New York.