Omandada kogemust ja saada tagasisidet keeruka otsustusülesande lahendamisel. Omandada teadmisi ja arendada oskusi MCDM valikumudelite (AHP ja ANP) teenustepõhise hajusarhitektuuri arendamisel ja äriprotsessiga integreerimisel, kasutades uudseid ja kaasaegseid MCDM mudelite andmevahetus-standardeid.

To gain experience and to receive feedback on solving a complicated decision-making task. To acquire knowledge and develop skills for the development of service-based distributed architecture of MCDM choice models (AHP and ANP) and their integration with a business process, using novel and contemporary data exchange standards for MCDM models.

Kursuse läbinud üliõpilased:
1. oskavad iseseisvalt lahendada keerukat valikuülesannet;
2. oskavad viia läbi ja hinnata mudeli tundlikkust Monte Carlo meetodi abil, teha tulemustest lõppjäreldusi;
3. oskavad realiseerida ja integreerida valdkonna keskset veebiteenust;
4. oskavad valikuülesande otsustusmudelit dokumenteerida ja kaitsta.

On completion of the course, the students:
1. can independently solve a complicated choice task;
2. can perform and evaluate the sensitivity of the model using the Monte Carlo method, and draw conclusions from the model results;
3. are able to implement and integrate a domain-specific web-service;
4. can document and defend a decision-making model for the choice task.

Tudeng peab iseseisvalt lahendama keeruka otsustusülesande rakendades vähemalt ühte järgnevatest otsustusanalüüsi meetoditest: fuzzy AHP, fuzzy ANP, paremusjärjestustel põhinev eelistuste mudeldamine. Analüüsi teostamiseks on vaja realiseerida ka vähemalt üks veebiteenus ja integreerida see teiste olemasolevate teenustega, kasutades uudseid ja kaasaegseid MCDM mudelite kohta käivaid andmevahetusstandardeid. Loodud veebiteenus tuleb integreerida äriprotsessiga, milleks võib olla näiteks e-riigi äriprotsess või GIS-põhine äriprotsess või ERP-põhine äriprotsess.

Student must solve a complicated decision-making task by using at least one of the following decision-making methods: fuzzy AHP, fuzzy ANP, preference modeling based on rankings. The student also must implement at least one web service and to integrate it with existing web solutions, using novel and contemporary data exchange standards for MCDM models. The implemented web service must be integrated with a business process, for instance an e-government business process or a GIS-based business process or an ERP-based business process.

Projekt. Projekt on võimalik edasi arendada magistritööks.

A course project. The project can be further developed for a Master's thesis.

4* 16 tundi praktikume ja harjutustund2 + 92 tundi iseseisvat tööd = 156 tundi. Iseseisev töö sisaldab mahukat iseseisvat kursuse projekti.

4* 16 h of practical work + 92 h of independent work = 156 h. Independent work includes extensive independent course project.

Compulsory literature:
1. Saaty, Thomas L., The Analytic Network Process: Decision Making With Dependence And Feedback, RWS Publications, USA, 1996.
2. Veskioja, Tarmo;Võhandu, Leo. Stable marriage problem and college admission. Tallinn : TUT Press. 2005.
3. DMN standard, http://www.omg.org/spec/DMN/About-DMN/
4. XMCDA standard, https://www.decision-deck.org/xmcda/
5. PMML standard, http://dmg.org/pmml/v4-3/GeneralStructure.html
Additional literature:
6. Cliff T. Ragsdale, Spreadsheet Modeling and Decision Analysis, 7th edition.
7. D.P. Kroese, T. Taimre, Z.I. Botev (2011). Handbook of Monte Carlo Methods, Wiley Series in Probability and Statistics, John Wiley and Sons, New York. https://people.smp.uq.edu.au/DirkKroese/montecarlohandbook/
8. M. J. Sigal and R. P. Chalmers, 2016. Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24, 136-156.
http://amstat.tandfonline.com/doi/full/10.1080/10691898.2016.1246953
https://cran.r-project.org/web/packages/MonteCarlo/index.html

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