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Lecture 11 – Stochastic Processes Topics • Definitions • Review of probability • Realization of a stochastic process • Continuous vs. discrete systems • Examples Basic Definitions Stochastic process: System that changes over time in an uncertain manner Examples • Automated teller machine (ATM) • Printed circuit board assembly operation • Runway activity at airport State: Snapshot of the system at some fixed point in time Transition: Movement from one state to another Elements of Probability Theory Experiment: Any situation where the outcome is uncertain. Sample Space, S: All possible outcomes of an experiment (we will call them the “state space”). Event: Any collection of outcomes (points) in the sample space. A collection of events E1, E2,…,En is said to be mutually exclusive if Ei Ej = for all i ≠ j = 1,…,n. Random Variable (RV): Function or procedure that assigns a real number to each outcome in the sample space. Cumulative Distribution Function (CDF), F(·): Probability distribution function for the random variable X such that F(a) Pr{X ≤ a} Components of Stochastic Model Time: Either continuous or discrete parameter. t0 t1 t2 t3 t4 time State: Describes the attributes of a system at some point in time. s = (s1, s2, . . . , sv); for ATM example s = (n) • Convenient to assign a unique nonnegative integer index to each possible value of the state vector. We call this X and require that for each s X. • For ATM example, X = n. • In general, Xt is a random variable. Model Components (continued) Activity: Takes some amount of time – duration. Culminates in an event. For ATM example service completion. Transition: Caused by an event and results in movement from one state to another. For ATM example, a a 0 1 d a 3 2 d a d d # = state, a = arrival, d = departure Stochastic Process: A collection of random variables {Xt}, where t T = {0, 1, 2, . . .}. Realization of the Process Deterministic Process Time between arrivals Pr{ ta } = 0, < 1 min Time for servicing customer Pr{ ts } = 0, < 0.75 min Arrivals occur every minute. = 1, 1 min = 1, 0.75 min Processing takes exactly 0.75 minutes. n Number in system, n 2 1 0 0 1 2 3 4 5 6 7 8 9 10 time (no transient response) Realization of the Process (continued) Stochastic Process Time for servicing a Pr{ ts } = 0, < 0.75 min customer = 0.6, 0.75 1.5 min = 1, 1.5 min n a 3 a 2 a 1 a d a d a a d a d d a a d Number in system, n d d d d 0 0 2 4 6 8 10 12 time Markovian Property Given that the present state is known, the conditional probability of the next state is independent of the states prior to the present state. Present state at time t is i: Xt = i Next state at time t + 1 is j: Xt+1 = j Conditional Probability Statement of Markovian Property: Pr{Xt+1 = j | X0 = k0, X1 = k1,…, Xt = i } = Pr{Xt+1 = j | Xt = i } for t = 0, 1,…, and all possible sequences i, j, k0, k1, . . . , kt–1 Interpretation: Given the present, the past is irrelevant in determining the future. Transitions for Markov Processes State space: S = {1, 2, . . . , m} Probability of going from state i to state j in one move: pij State-transition matrix P p11 p21 p12 p22 p1m p2m m pm1 pm2 p 1 2 mm P pij Theoretical requirements: 0 pij 1, j pij = 1, i = 1,…,m Discrete-Time Markov Chain • A discrete state space • Markovian property for transitions • One-step transition probabilities, pij, remain constant over time (stationary) Simple Example State-transition matrix 0 1 State-transition diagram (0.6) 2 0 P = 0 0.6 0.3 0.1 1 0.8 0.2 0 2 1 0 0 (0.1) (0.3) (1) 2 (0.8) 1 (0.2) Game of Craps • Roll 2 dice • Outcomes – Win = 7 or 11 – Loose = 2, 3, 12 – Point = 4, 5, 6, 8, 9, 10 • If point, then roll again. – Win if point – Loose if 7 – Otherwise roll again, and so on (There are other possible bets not included here.) State-Transition Network for Craps not (4,7) not (5,7) not (6,7) not (8,7) not (9,7) not (10,7) P4 P5 P6 P8 P9 P10 4 5 Win 6 8 10 9 7 5 6 4 (7, 11) Start 7 8 9 7 10 (2, 3, 12) 7 7 Lose 7 Transition Matrix for Game of Craps Sum 2 Prob. 3 4 5 6 7 8 9 10 11 12 0.028 0.056 0.083 0.111 0.139 0.167 0.139 0.111 0.083 0.056 0.028 Probability of win = Pr{ 7 or 11 } = 0.167 + 0.056 = 0.223 Probability of loss = Pr{ 2, 3, 12 } = 0.028 + 0.056 + 0.028 = 0.112 Start P= Win Lose P4 P5 P6 P8 P9 P10 Start 0 0.222 0.111 0.083 0.111 0.139 0.139 0.111 0.083 Win 0 1 0 0 0 0 0 0 0 Lose 0 0 1 0 0 0 0 0 0 P4 0 0.083 0.167 0.75 0 0 0 0 0 P5 0 0.111 0.167 0 0.722 0 0 0 0 P6 0 0.139 0.167 0 0 0.694 0 0 0 P8 0 0.139 0.167 0 0 0 0.694 0 0 P9 0 0.111 0.167 0 0 0 0 0.722 0 P10 0 0.083 0.167 0 0 0 0 0 0.75 Examples of Stochastic Processes Single stage assembly process with single worker, no queue a State = 0, worker is idle 0 1 State = 1, worker is busy d Multistage assembly process with single worker, no queue a 0 d1 1 d2 2 d3 3 d4 4 5 d5 State = 0, worker is idle State = k, worker is performing operation k = 1, . . . , 5 Examples (continued) Multistage assembly process with single worker and queue (Assume 3 stages only; i.e., 3 operations) s1 number of parts in the system s = (s1, s2) where s2 current operation being performed (1,3) Operations k = 1, 2, 3 (2,3) a d3 d3 d2 a a d2 (2,2) (3,2) a d1 d1 a … d1 (2,1) (1,1) … d3 d2 (1,2) (0,0) (3,3) a a (3,1) … Single Stage Process with Two Servers and Queue 0, if server i is idle s = (s1, s2 , s3) where si 1, if server i is busy i = 1, 2 s3 number in the queue Arrivals d1 1 … d2 2 (1,0,0) Statetransition network d1 0 (0,0,0) a 1 d2 a d1 d2 2 (0,1,0) a d1 ,d2 d1 , d2 3 (1,1,0) 4 a 5 a (1,1,1) (1,1,2) • • • Series System with No Queues Arrivals Transfer 1 Component Notation State s = (s1, s2 , s3) 2 Transfer 3 Finished Definition 0, if server i is idle si 1, if server i is busy for i 1, 2,3 State space S = { (0,0,0), (1,0,0), . . . , (0,1,1), (1,1,1) } The state space consists of all possible binary vectors of 3 components. Events Y = {a, d1, d2 , d3} a = arrival at operation 1 dj = completion of operation j for j = 1, 2, 3 What You Should Know About Stochastic Processes • Definition of a state and an event. • Meaning of realization of a system (stationary vs. transient). • Definition of the state-transition matrix. • How to draw a state-transition network. • Difference between a continuous and discrete-time system. • Common applications with multiple stages and servers.