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Compared to the previous semester no changes needed to be implemented.The students responded very well to the structure of this course. It consisted of approximately 30 hours video lectures, 10 hours lectures via Zoom, Regular Q&amp;A sessions (once a week) and physical tutorials every second week.The feedback from the students is very good. At the moment, there are no plans to make changes to the curriculum or composition of the course.

The feedback from the students and the student representatives have been very positive with respect to the learning environment and the students learning achievements.The grades in this course are very good, which implies that the students' learning outcome is also very good.Most of the students deliver ambitious mandatory assignments were they apply the methods that they have learning in this course.

NTNU:TIA:REST

NTNU Handelshøyskolen

NTNU Business School

Alle trykte og håndskrevne hjelpemidler tillatt. Alle kalkulatorer tillatt.

Engelsk

Mer om vurdering

Emnet undervises på engelsk. Se engelsk emnebeskrivelse for mer informasjon.

Forkunnskapskrav

Faglig innhold

Kursmateriell

Læringsutbytte

Læringsformer og aktiviteter

To obligatoriske innleveringer

Decision Modelling and Optimization

Økonomi og administrasjon

Bachelor i økonomi og administrasjon

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The digital part of the course was extended. The students respond very positive to this offer.A few topics were upgraded, particularly the part on dynamic programming because of its relevance for more advanced techniques in machine learning.Other approaches like linear programming, non-linear programming are as relevant as ever and are updated only minimally.

The feedback from the students and the student representatives have been very positive with respect to the learning environment and the students learning achievements.The grades in this course are very good, which implies that the students' learning outcome is also very good.Most of the students deliver ambitious mandatory assignments were they apply the methods that they have learned in this course.

At the exam, some or all exercises need to be implemented with Microsoft Excel.The exam follows the examination regulations of NTNU.In case of postponed examination (continuation examination), written examination may be changed to oral examination.

This course requires elementary courses in mathematics and managerial economics.

The following topics are taught in this course:<ul><li>Linear optimization (graphical method, Simplex method, sensitivity analysis and duality)</li><li>Integer programming (Branch and Bound),</li><li>Network models (transport, transshipment, travelling salesman, shortest path and similar models),</li><li>Non-linear optimization (with/without constraints, gradient descent method, KKT conditions),</li><li>Decision tree model,</li><li>Introduction to dynamic optimization and reinforcement learning.</li></ul>Most of the models will be implemented by means of Excel spreadsheets and Excel solver.

The curriculum will be announced when the course commences.

Knowledge: The student<ul><li>learns about the variety of practical decision problems that can be described by means of quantitative models. </li><li>receives knowledge about necessary components and properties of quantitative decision models. </li><li>obtains knowledge about solution methods or algorithms that are useful to find solutions to decision problems and models.</li></ul>Skills: The student will be enabled <ol><li>to translate practical decision problems into quantitative models</li><li>to analyze the properties of decision models </li><li>to apply appropriate methods for finding solutions to decision models, </li><li>to implement decision models with software, </li><li>to transform complex models into models that are better accessible by solver software.</li></ol>General Competence: The student learns how to apply his knowledge and skills in different practical situations. He will be encouraged to reflect about advantages, shortcomings and further reaching implications of his models and solutions.

Lectures, videos, written exercises and data exercises.

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70% av Forelesningene ble gjennomført via Zoom og 30% via video.Øvingene ble gjennomført med Zoom.Alle Zoom-forelesninger ble lagt ut som opptak (unntatt 2 forelesninger, der det oppstå tekniske problemer ved opptaket).Det har vært veldig gode tilbakemeldinger fra studentene, både angående innhold, relevans for deres studier og angående former av kunnskapsoverføring.Både studenter og foreleseren drives med dette faget.

Læringsutbyttet er oppdatert og relevant. I år har vi begynt å legge inn et fremtidsrettet tema innen maskinlæring.Læringsutbyttet kommuniseres via forelesningsslides for hvert tema (skriftlig) og for hver enkel forelesning (muntlig på zoom).Læringsutbytte kobles opp mot andre fag innen finans, investering, logistikk og økonomistyring. Det brukes forkunnskap fra statistikk og matematikk.

Læringsutbyttet oppnås gjennom forelesninger (denne gangen digitalt med zoom), obligatoriske innleveringer, øvinger (frivillige), videoer og implementering med software.Gjennom feedback (epost, spørsmål i Zoom chat) og samtaler med studenter (teams) oppdager jeg at studentene er motiverte og at egeninnsatsen er høy (men varierende). Innleverte arbeidskrav er av høy kvalitet og jeg ser at studentene jobber jevnt og kontinuerlig med faget gjennom semestret..Læringsmiljøet vurderes som godt. Både studentene og foreleseren drives med dette faget.

I mitt fag legges det vekt på og vurderes bestandig at det er samsvar mellom vurderingsform og læringsutbyttet. Studentene bruker digitale verktøy (Excel, Desmos) under eksamen. Hver enkelt student få en eksamen med egne tall for å forebygge samarbeid/fusk under eksamen.

Ingen trykte eller håndskrevne hjelpemidler tillatt. Bestemt, enkel kalkulator tillatt.

Supporting material allowed on exams: Approved calculator regarding NTNUs support material code B-D "specific basic calculator". Other calculators that are allowed in the course are: Casio FC-100V and Texas Instruments - BAII Plus. It is possible that parts of the exercises need to be implemented with Microsoft Excel. In this case a PC with Microsoft Excel will be provided and set up by NTNU. No individual printed, hand written or electronically saved materials allowed. No Internet connection allowed. No network access or access to home and user directories from PC allowed The exam follows the examination regulations of NTNU. Requires admission to study program, see "special conditions".In case of postponed examination (continuation examination), written examination may be changed to oral examination.

The following topics are taught in this course:
- Linear optimization,
- Simplex method,
- Sensitivity analysis and duality in linear optimization,
- Integer programming,
- Network models (Transport, transshipment, travelling salesman, shortest path and similar models),
- Non-linear optimization (with and without constraints),
- Gradient, steepest descent, and KKT-based algorithms,
- Branch and Bound,
- Decision tree model,
- Introduction to dynamic optimization (optional),
- Introduction to stochastic optimization (optional).
Most of the models will be implemented by means of Excel spreadsheets and solver.

Lectures, written exercises and data exercises.

Knowledge:
The student learns about the variety of practical decision problems that can be described by means of quantitative models.
The student receives knowledge about necessary components and properties of quantitative decision models.
The student obtains knowledge about solution methods or algorithms that are useful to find solutions to decision problems and models.

Skills:
The student will be enabled 
(1) to translate practical decision problems into quantitative models,
(2) to analyse the properties of decision models,
(3) to apply appropriate methods for finding solutions to decision models,
(4) to implement decision models with software,
(5) to transform complex models into models that are better accessible by solver software.

General Competence:
The student learns how to apply his knowledge and skills in different practical situations. He will be encouraged to reflect about advantages, shortcomings and further reaching implications of his models and solutions.

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Generelt har det vært veldig gode tilbakemeldinger fra studentene, både angående innhold, relevans for deres studier og angående former av kunnskapsoverføring.Både studenter og foreleseren drives med dette faget.

Læringsutbyttet er oppdatert og relevant.Læringsutbyttet kommuniseres via forelesningsslides for hvert tema (skriftlig) og for hver enkel forelesning (muntlig). Fremdriftsplanen oppdateres kontinuerlig på Blackboard.Læringsutbytte kobles opp mot andre fag innen økonomi som mikroøkonomi, statistikk, matematikk, økonomistyring og finans, men også mot andre områder som telekommunikasjon, transport og logistikk.

Læringsutbyttet oppnås gjennom forelesninger, obligatoriske innleveringer, øvinger (frivillige), videoer og implementering med software.Gjennom feedback (epost, spørsmål) og samtaler med studenter oppdager jeg at studentene er motiverte og at egeninnsatsen er høy (men varierende). Innleverte arbeidskrav er av høy kvalitet og jeg ser at studentene jobber jevnt og kontinuerlig med faget gjennom semestret..Læringsmiljøet vurderes som godt. Både studentene og foreleseren drives med dette faget.

I mitt fag legges det vekt på og vurderes bestandig at det er samsvar mellom vurderingsform og læringsutbyttet.

Godkjent kalkulator

Anbefalte forkunnskaper

Examination aids: Calculator type 2 according to NTNU Business School calculator rules.
It is possible that parts of the exercises need to be implemented with Microsoft Excel. In this case a PC with Microsoft Excel will be provided and set up by NTNU. No individual printed, hand written or electronically saved materials allowed. No Internet connection allowed. No network access or access to home and user directories from PC allowed The exam follows the examination regulations of NTNU.



The following topics are taught in this course:
- Linear optimization,
- Simplex method,
- Sensitivity analysis and duality in linear optimization,
- Integer programming,
- Network models (Transport, transshipment, travelling salesman, shortest path and similar models),
- Non-linear optimization (with and without constraints),
- Gradient, steepest descent, and KKT-based algorithms,
- Decision tree model,
- Branch and Bound,
- Simulation,
- Introduction to dynamic optimization (optional),
- Introduction to stochastic optimization (optional).
Most of the models will be implemented by means of Excel spreadsheets and solver.

The student learns 
(1) to translate practical decision problems into quantitative models,
(2) to analyse the properties of decision models,
(3) to apply appropriate methods for finding solutions to decision models,
(4) to implement decision models with software,
(5) to transform complex models into models that are better accessible by solver software.
 
 

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Decision Modelling and Optimization - BØA2020