Supply Chain Optimization, 6 credits
Optimering av försörjningskedjor, 6 hp
TAOP18
Main field of study
Mathematics Applied Mathematics Industrial Engineering and ManagementCourse level
Second cycleCourse type
Programme courseExaminer
Nils-Hassan QuttinehDirector of studies or equivalent
Nils-Hassan QuttinehEducation components
Preliminary scheduled hours: 44 hRecommended self-study hours: 116 h
Available for exchange students
YesMain field of study
Mathematics, Applied Mathematics, Industrial Engineering and ManagementCourse level
Second cycleAdvancement level
A1XCourse offered for
- Master's Programme in Mathematics
- Master's Programme in Mechanical Engineering
- Mathematics, Bachelor's Programme
- Design and Product Development
- Industrial Engineering and Management - International, M Sc in Engineering
- Industrial Engineering and Management, M Sc in Engineering
- Mechanical Engineering, M Sc in Engineering
- Master's Programme in Industrial Engineering and Management
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
(valid for students admitted to programmes within which the course is offered)
Basic course in optimization, including network programming. Solid knowledge in computer programming. Recommended prerequisite: Knowledge in optimization modelling language (AMPL or ZIMPL), large scale optimization.
Intended learning outcomes
The course aims to give the students an ability to model optimization problems, and an insight in how mathematical theory can be used to formulate and solve practical problems, with emphasis on applications in supply chain, distribution and transportation planning. The course also aims to give a deeper knowledge about combinatorial optimization, i.e. optimization problems with an underlying graph structure.
Course content
Supply chain optimzation problems, Sequencing and scheduling problems in production planning, Classical machine scheduling problems, Capacitated lot-sizing problem, Transportation and routing problems, Local search/tabu search, Column generation, Ampl-modelling.
Teaching and working methods
The course is built up around a number of cases (practical applications), where the students work with problem analysis, modelling and solving using software as Matlab and Ampl/Cplex. The lectures cover theory and optimization methodology. The cases are discussed and the students present the results of their work. Other practical applications are discussed.
Examination
PRA1 | Oral and written presentation of case studies | 6 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
Supplementary courses: Mathematical Optimization
Department
Matematiska institutionenDirector of Studies or equivalent
Nils-Hassan QuttinehExaminer
Nils-Hassan QuttinehCourse website and other links
http://courses.mai.liu.se/GU/TAOP18Education components
Preliminary scheduled hours: 44 hRecommended self-study hours: 116 h
Course literature
Other
- Kursmaterial från institutionen
Code | Name | Scope | Grading scale |
---|---|---|---|
PRA1 | Oral and written presentation of case studies | 6 credits | U, 3, 4, 5 |
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva.
Other
Note: The course matrix might contain more information in Swedish.
I | U | A | Modules | Comment | ||
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1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
1.1 Knowledge of underlying mathematics and science (G1X level) |
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X
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X
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PRA1
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1.2 Fundamental engineering knowledge (G1X level) |
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X
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1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
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1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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1.5 Insight into current research and development work |
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2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
2.1 Analytical reasoning and problem solving |
X
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X
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X
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PRA1
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2.2 Experimentation, investigation, and knowledge discovery |
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2.3 System thinking |
X
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PRA1
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2.4 Attitudes, thought, and learning |
X
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PRA1
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2.5 Ethics, equity, and other responsibilities |
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3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
3.1 Teamwork |
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X
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PRA1
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3.2 Communications |
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X
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PRA1
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3.3 Communication in foreign languages |
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4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
4.1 External, societal, and environmental context |
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4.2 Enterprise and business context |
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4.3 Conceiving, system engineering and management |
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X
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PRA1
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4.4 Designing |
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4.5 Implementing |
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4.6 Operating |
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5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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5.2 Economic conditions for knowledge development |
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5.3 Identification of needs, structuring and planning of research or development projects |
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5.4 Execution of research or development projects |
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5.5 Presentation and evaluation of research or development projects |
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This tab contains public material from the course room in Lisam. The information published here is not legally binding, such material can be found under the other tabs on this page. Click on a file to download and open it.
Name | File name | Description |
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Courseinfo_TAOP18_2019 | Courseinfo_TAOP18_2019.pdf |