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Fondamenti di Ricerca Operativa
Federico Malucelli (lezioni + esercitazioni)Stefano Gualandi (esercitazioni + laboratorio)
Fabio Panozzo (laboratorio)
Wednesday, September 19, 2012
What is Operations Research (OR)?Support in Decision Making
in any complex systemHOW?
Mathematical modelsQuantitative methodsEfficient algorithms
Use the science to help in taking decisions
Maths
Computer Science
Wednesday, September 19, 2012
What kind of decisions?
Strategic level:e.g. design a railroadTactical level:e.g. how many trains to useOperational level:e.g. schedule the trains
At any level from top management to everyday problems
Wednesday, September 19, 2012
Examples
One colleague of yours has been hired by a bank in LondonHe proposed an OR model to allocate bonds to portfoliosAnother student had to organize the tutoring in a climbing centerHe proposed an OR model to maximize the opening hours
OR is really helpful!Wednesday, September 19, 2012
Optimization is as old as humanity
Dido and the foundation of Carthage (814 BC)
Isoperimetric problem
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Practical cases of OR application
Why did you enroll in this course?What route did you follow to reach Politecnico?Where do you eat and what do you eat?
Probably you do not apply consciously OR methodsbut you solve OR problems
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One important adviceOR is easier to learn if you put your hands on the problems
componentsPhone AScore A
Phone BScore BObjective:
Maximize the overall scoreWednesday, September 19, 2012
Solution
Are you sure that the solution that you found is the best one?How can you certify it?How much are you willing to spend to have one more display, or keyboard, or memory board?
Wednesday, September 19, 2012
How OR approaches the problem
DecisionsWhat decisions did you take?How do you characterize your solution?RulesHow do you combine components to obtain a product?ObjectiveWhat do you have to optimize?
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Decisions = variables
xA = how many phones of type A
xB = how many phones of type B
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Rules = ConstraintsThe number of used components cannot exceed the available ones
1xA + 2xB ! 10 display2xA + 2xB ! 18 memory1xA + 3xB ! 12 transmission2xA + 3xB ! 21 keyboard1xA ! 9 navigation1xA ! 10 cameraxA, xB ! 0 non negativity
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Objective function
Maximize the overall score
max 3xA + 8xB
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One simple solution methodExploit the fact that we have only 2 variables
Give a geometric representation
xA
xB
5
1xA + 2xB ! 10
10
9
9
2xA + 2xB ! 18
4
12
xA + 3xB ! 12
7
2xA + 3xB ! 21 xA ! 9
xA ! 10
Feasible regioninfinite number of solutions
“Interesting” solutionsxA = 0, xB = 4 v = 32
xA = 6, xB = 2 v = 34
xA = 8, xB = 1 v = 32
xA = 9, xB = 0 v = 27
Wednesday, September 19, 2012
Interesting web sites
http://www.scienceofbetter.org/classification of success applicationshttp://www.informs.org/American OR societyFranz Edelman Award recordhttp://www.airo.org/Italian OR association
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Optimization in Public Transport
ATM surface vehicle control room
Wednesday, September 19, 2012
Timetable design
A C B
bus line
relief point
C
B
A
8:00 10:00 14:0012:00 18:0016:00 22:0020:00
Wednesday, September 19, 2012
Vehicle Scheduling
C
B
A
8:00 10:00 14:0012:00 18:0016:00 22:0020:00
Wednesday, September 19, 2012
Crew Scheduling
C
B
A
8:00 10:00 14:0012:00 18:0016:00 22:0020:00
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Success applications of ORYear Company Application Result
90 Taco Bell personnel scheduling 7.6 M$ saved per year
92 Harris Semiconductors production planning from 50% to 95%jobs on time
95 GM - car rental division car fleet distribution avoided the business failure
96 HP - printers re-planned the production plant production doubled
99 IBM reorganized the logistic chain 750M$ saved per year
2007 Dutch railways timetable design Increased punctuality and customer satisfaction
2010 Petrobras Helicopter scheduling 20M$ saved per year
Wednesday, September 19, 2012
Games
or any other fantasy sportWednesday, September 19, 2012
Fantasy sportset up a team buying real players following some rules (number of backwards, forwards, etc.)
limited budget of fantasy money
each week depending on the players stats of the last game played a score is attributed to each team
Where does OR comes into play?
pointer to an article about OR and fantasy baseballhttp://lionhrtpub.com/orms/orms-8-10/frfantasy.html
We will study models and algorithms for approaching the problem
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Skills of an OR expert
Analyze the problemKnowledge of the application fieldCommunication skills to understand and explain
Knowledge of the OR methods
Ability in building mathematical models
Interpret the results
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Program of the courseIntroduction to OROptimization problems and their formulations; decision variables, objective function, constraints. Modeling techniques
Graphs and network flows problemsTrees and graphs. Shortest paths, maximum flow, minimum cost flow, assignments. Some data structures and solution algorithms. Complexity analysis
Linear ProgrammingDuality theory, complementary slackness, the simplex method; geometrical and economical interpretation. Basic solutions and optimality conditions
Integer Linear Programming and combinatorial optimizationRelaxations and branch and bound algorithm. Heuristic algorithms: greedy and local search.
Basics of Non Linear ProgrammingUnconstrained optimization. Convex programming. Newton method, gradient method, conjugate gradient method. Constrained optimization.
Wednesday, September 19, 2012
Organizzazione del corsoAttività di laboratorio (10 ore): modellazioneutilizzo di un pacchetto software (scaricabile)
Esame scritto: 28 punti
2 Prove in itinereSe entrambe sufficienti esonerano dallo scrittoUna insufficienza/assenza è recuperabile (1 sola volta) a qualsiasi esame scritto (fa fede la consegna)
Esame di laboratorio opzionale (4 punti) [on line]
Punti bonus (max 2 a testa): domande/giochi durante le lezioni
Appunti ed esercizi svolti disponibili sul sito del corsohttp://home.dei.polimi.it/malucell
Ricevimento: Mercoledì dalle 16 alle 18
Wednesday, September 19, 2012
Forward pointersNon linear optimizationDiscrete optimization
OttimizzazioneLM - second semester
Graph OptimizationLM (TLC) - second semester
Network designGraph algorithms
Wednesday, September 19, 2012
Master thesis in optimizationOptimize the home healthcare service(in collaboration with a health service provider)
Design of broadband access networks
Network design problems with Shared Risk Group protection
Special design and routing problems in transportationin collaboration with ATM Milano
Timetable design problems
Optimization in food production
Classical combinatorial optimization problems
Wednesday, September 19, 2012