Facts about the course

ECTS Credits:
2.5
Responsible department:
Faculty of Logistics
Course Leader:
Erik Langelo
Lecture Semester:
Autumn
Teaching language:
English
Duration:
1 week

LOG904-165 Models in Dynamic Lot Sizing (Autumn 2021)

About the course

This seminar gives an overview of dynamic lot size models used in production management. The beginning of the seminar will focus on the seminal model of Wagner and Whitin. Subsequent parts discuss capacitated models and models with pricing, including the aspect of pricing with respect to new technology.

Topics covered: Uncapacitated lot sizing with different formulations. The Wagner-Whitin algorithm and heuristics. Lot sizing with pricing; uncapacitated and capacitated. Various extensions of these models. All models are discrete-time and finite-horizon.

The course is connected to the following study programs

Recommended requirements

Knowledge of AMPL, some familiarity with mathematical models and algorithms in general.

The student's learning outcomes after completing the course

After the seminar, students will be able to link practical problems to their corresponding lot size model and find solutions to these problems, using either a commercial solver or a specialized algorithm. Students will be able to describe the differences and similarities between the various models, program the models in AMPL and interpret solutions.

Forms of teaching and learning

Monday-Thursday: lectures at 9:15-12:00, computer lab at 13:15-16:00. Friday: Oral exams based on the assignment.

Examination

Form of assessment: Home assessment without presentation

  • Proportion: 60%

  • Duration: -

  • Grouping: Group

  • Grading scale: Letter (A - F)

  • Support material: All printed and written supporting material

Form of assessment: Oral exam

  • Proportion: 40%

  • Duration: -

  • Grouping:

  • Grading scale: Letter (A - F)

  • Support material: All printed and written supporting material

Syllabus

TBA

Last updated from FS (Common Student System) Oct. 24, 2021 8:20:25 AM