sf2863 systems engineering, 7.5 hp
TRANSCRIPT
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SF2863 Systems Engineering, 7.5 HP
Lecturer: Per Enqvist
Optimization and Systems TheoryDepartment of Mathematics
KTH Royal Institute of Technology
November 5, 2013
P. Enqvist Systems Engineering
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Systems Engineering (SF2863) 7.5 HP
1 Course Information
2 Examples of Applications
3 Introduction to Markov Chains
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Teachers
Per Enqvist (Email: [email protected])Phone: 790 62 98Hildur Æsa Oddsdóttir (Email: [email protected])Phone: 790 84 57Göran Svensson (Email: [email protected])Phone: 790 66 59
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Literature
Main Literature: “Introduction to operations research”, Ninthedition, by Hillier and Lieberman.
Should be available at the KTH Bookshop
The following material is sold at the math student office, Lindstedtsv25.
Exercises in SF2863 Systems Engineering, 2011.
Further material will be posted on the homepage.
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Course homepage
On the course homepagehttp://www.math.kth.se/optsyst/grundutbildning/kurser/SF2863/you can find
1 a preliminary schedule2 reading instructions, recommended exercises etc.3 home assignments, rules and information about deadlines4 these slides
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Home assignments
There will be two voluntary home assignments.
HA 1: Markov chain/process example - the ferry (2 bonus points)HA 2: Spare parts optimization (4 bonus points)
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Exam
The maximal result on the exam (not counting bonus points) is 50points.Preliminary grade limits:
Grade A B C D E FXPoints 43-50 38-42 33-37 28-32 25-27 23-24
At the exam a brief formula sheet will be handed out. No othertools, such as calculators, are allowed.The first written exam is January 13, 2014, at 14.00-19.00.It is necessary to sign up for the exam, and it can be done on “Mypages”, Nov. 25 - Dec. 8.
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Course Information
Course in Systems Engineering with Introduction to MarkovChain/Process theory.
“We use statistics, probability theoryand differential/difference equations tobuild mathematical models for processes,combine them to complex systems, ana-lyze them and optimize to find the bestcontrol/management policy.”
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Course elements
1 Markov chains/processes2 Queueing theory3 Spare parts optimization4 Marginal Allocation5 Deterministic/Stochastic Inventory theory6 Dynamic Programming7 Markov Decision Processes
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Spare Parts Optimization
How many spareparts of each type should be held, in which location,and where should they be repaired?
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Newsboy Problem
How many newspapers should the salesman buy each day ?
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Marginal Allocation Problem
Where should you use redundance to get the best reliability ?(relative to the weight)
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Queueing Theory
What is the expected time waiting in a queue?
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Markov Chain
Example with two states, ’E’ and ’A’.
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Examples
Markov Chains with state Xt where t = 0,1,2, · · · .
Xt = wind condition {1 = Calm,2 = breeze,3 = storm, }at a particular place on day t .Xt = number of items in stock of a particular item on day t .Xt = accumulated sum of points after t rolls of a die.Xt = number of rabbits living on Gärdet at time t .Xt = number of complaint phone calls to the help desk at day t .Xt = condition of patient {1 = stable,2 = manic,3 = depressive}on day t .
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Some useful results from probability theory
The conditional probability of A given B is defined by
P(A|B) =P(A ∩ B)
P(B)
Law of total probabilityIf Bn form a partition of the sample space, i.e., the Bn are disjoint andthe union is the whole sample space, then
Pr(A) =∑
n
Pr(A|Bn)Pr(Bn)
In particular, if X and Y are discrete valued stochastic variables, then
Pr(X = x) =∑
y
Pr(X = x |Y = y)Pr(Y = y)
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