IE221: Operations Research Probabilistic Methods
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1 IE221: Operations Research Probabilistic Methods Fall 2001 Lehigh University IMSE Department Tue, 28 Aug 2001 IE221: Lecture 1 1 History of OR Britain, WWII (1938). Multi-disciplinary team of scientists explore how to use radar information to deploy and use fighter planes. United States. Mathematical models (Search Theory) used to develop optimal air search patterns for anti-submarine tactics. Tue, 28 Aug 2001 IE221: Lecture 1 2 1
2 Evolution of OR OR moves into industrial domain (1950 s), parallels computers growth as business planning/management tool computer core memory Focus on development of mathematical modeling techniques to improve or optimize real- world systems. Tue, 28 Aug 2001 IE221: Lecture 1 3 What is Operations Research? Before: application of mathematics and the scientific method to military operations Winchester Warehouse, Winchester, KY photo from www. winchesterwarehouse.com Today: scientific approach to decision making. Seeks to determine best way to design and operate system, usually requiring allocation of scarce resources Tue, 28 Aug 2001 IE221: Lecture 1 4 2
3 Career Opportunities Accounting Actuarial Work Computer Services Corporate Planning Economic Analysis Financial Modeling Industrial Engineering Investment Analysis Logistics Manufacturing Services Management Consulting Management Training Market Research Operations Research Policy Planning Production Engineering Quantitative Methods Strategic Planning Systems Analysis Transportation Tue, 28 Aug 2001 IE221: Lecture 1 5 The OR Methodology Formulate the Problem Verify the model and use it for Prediction Observe the System Formulate a Mathematical Model of the Problem Present Results to Organization Select a Suitable Alternative Implement and Evaluate Recommendations Tue, 28 Aug 2001 IE221: Lecture 1 6 3
4 The OR Methodology Formulate the Problem Verify the model and use it for Prediction Observe the System Formulate a Mathematical Model of the Problem Present Results to Organization Select a Suitable Alternative Implement and Evaluate Recommendations Tue, 28 Aug 2001 IE221: Lecture 1 7 Probabilistic Inventory Models Steinway showroom. GOAL: to minimize costs associated with maintaining inventory and meeting customer demand. Q: When should an order be placed? Q: How large should each order be? Tue, 28 Aug 2001 IE221: Lecture 1 8 4
5 PIM: News Vendor Problem DECIDE: q, number of newspapers to order. Demand D is a random variable. D=d w.p. P(d). Cost incurred is c(d,q). Excess newspapers are worthless. Profit lost if vendor is understocked. Photo by Bruce Takeo Akizuki Tue, 28 Aug 2001 IE221: Lecture 1 9 Probabilistic Dynamic Programming Models St. Petersburg rapid chess club. International master Evgenija Ovod, winner of 1999 Women s Cup. Decision required at each problem stage. Several states at each stage. Decision chosen describes transformation from current stage to next stage. Principle of optimality: optimal decision for each remaining stage must not depend on previous states. Recursion links cost or reward at stage t to that of stage t+1 Tue, 28 Aug 2001 IE221: Lecture
6 PDP: Parking Problem GOAL: Park as close to terminal as possible. At each empty space, decide whether to continue. No backtracking allowed. Charlottetown Airport Parking If not parked by the time terminal is reached, must enter paid parking lot at cost $M. Tue, 28 Aug 2001 IE221: Lecture 1 11 Markov Chain Models A Markov Chain is one type of discrete-time stochastic process. Memoryless Property: The probability distribution of state at time t+1 only depends on the state at time t. Stationarity Assumption: Transition probability from state i to j is independent of time t. Tue, 28 Aug 2001 IE221: Lecture
7 Efficient Market Hypothesis EMH: The daily price of a stock can be described as a Markov Chain, i.e. the probability distribution of tomorrow s price for one share of the stock depends only on today s price. Tue, 28 Aug 2001 IE221: Lecture 1 13 Queueing Theory Arrival and service process, service discipline, method for joining queue. Fraction of time idle? E(customers in queue)? E(time in queue) Distribution of number of customers in queue? PDF of waiting time? Number of servers? Queueing monks from webshots.com Tue, 28 Aug 2001 IE221: Lecture
8 Urban Emergency Services Server is ambulance Incidents (1 emergency patient) arrive as Poisson process What will service time depend on? District Tue, 28 Aug 2001 IE221: Lecture
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