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Optimization (3 cr)

Code: TZLM5300-3006

General information


Enrollment

01.08.2023 - 24.08.2023

Timing

28.08.2023 - 13.10.2023

Number of ECTS credits allocated

3 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Main Campus

Teaching languages

  • Finnish

Seats

0 - 50

Degree programmes

  • Bachelor's Degree Programme in Logistics

Teachers

  • Pasi Lehtola

Scheduling groups

  • TLS21SA (Capacity: 30. Open UAS: 0.)
  • TLS21SB (Capacity: 30. Open UAS: 0.)

Groups

  • TLS21S1
    Logistiikan tutkinto-ohjelma (AMK)

Small groups

  • TLS21SA
  • TLS21SB
  • 29.08.2023 11:30 - 13:30, Optimointi TZLM5300-3006
  • 29.08.2023 13:45 - 15:45, Optimointi TZLM5300-3006
  • 05.09.2023 11:30 - 13:30, Optimointi TZLM5300-3006
  • 05.09.2023 13:45 - 15:45, Optimointi TZLM5300-3006
  • 12.09.2023 11:30 - 13:30, Optimointi TZLM5300-3006
  • 12.09.2023 13:45 - 15:45, Optimointi TZLM5300-3006
  • 19.09.2023 11:30 - 13:30, Optimointi TZLM5300-3006
  • 19.09.2023 13:45 - 15:45, Optimointi TZLM5300-3006
  • 26.09.2023 11:30 - 13:30, Optimointi TZLM5300-3006
  • 26.09.2023 13:45 - 15:45, Optimointi TZLM5300-3006
  • 03.10.2023 11:30 - 13:30, Optimointi TZLM5300-3006 KOE
  • 03.10.2023 13:45 - 15:45, Optimointi TZLM5300-3006 KOE
  • 04.10.2023 11:00 - 13:00, Optimointi TZLM5300-3006 UUSINTAKOE

Objectives

Purpose of the course
After the course you will understand how engineers use mathematical models and tools to find the best, fastest or most economical way to achieve the desired operative goals.

Learning objectives
You know basic principles and methods of optimization and networks. You can construct an optimization or network model of a work-related problem and solve it by using a computer.

Content

Principles and methods of linear and nonlinear optimization
Fundamental graph theory and network optimization algorithms
Applications in operative decision making
Using Excel

Learning materials and recommended literature

Taylor, B. W. (2018) Introduction to Management Science. Pearson
Other material accessible in Moodle

Teaching methods

The contact lessons are in a computer class and involve use of computers. The theory should be independently acquired before class exercises. The learning is accomplished by assignments where theory is put into practice.

Exam dates and retake possibilities

The date and execution of the exam will be announced in the beginning of the course and in Moodle.

Alternative completion methods

The admission procedures are described in the degree rule and the study guide.

Student workload

Contact lessons about 20 hours
Independent study about 20 hours
Learning tasks about 20 hours

Further information for students

The assessment is based on learning tasks and exams.

An equivalent course in English TZLM5300-3007 Optimization.

Open AMK: at most 5 students if there are seats in the classroom.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Sufficient 1
You have achieved the desired goals. You know a few of the concepts and methods and how to apply them in familiar situations but your reasoning is often deficient and you make mistakes in calculations.

Satisfactory 2
You have achieved the desired goals. You know many of the concepts and methods and how to apply them in familiar situations but your reasoning is sometimes deficient or you make mistakes in calculations.

Evaluation criteria, good (3-4)

Good 3
You have achieved the desired goals. You know most of the concepts and methods and how to apply them in familiar situations showing often the ability to reason completely and calculate flawlessly

Very good 4
You have achieved the desired goals. You know most of the concepts and methods and how to apply them in new situations showing in most cases the ability to reason completely and calculate flawlessly.

Evaluation criteria, excellent (5)

Excellent 5
You have achieved the desired goals. You know all the concepts and methods and how to apply them in new situations showing always the ability to combine things, reason completely and calculate flawlessly.

Prerequisites

You can find the extreme values of a function by differentiation. You know the normal distribution.