This course provides a practical introduction to mathematical modeling and optimization in Excel and Python, using
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Excel Solver in Excel
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PuLP and default free solvers through PuLP in Python
A concrete use case serves as application example: Mathematical optimization of machine setup and changeover sequences. There are many versions of this problem, but in course we will focus on two single machine setup and changeover sequencing problems:
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Optimal setup and changeover sequence for a one-time production program
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Optimal changeover sequence for a repeated production cycle, i.e. repetitive cyclic production program
As part of this course, you will see and learn
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How to formally define a changeover sequencing problem mathematically
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Get an overview of modeling frameworks and solvers in Excel and Python
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How to setup Excel Solver and how to implement mathematical models with Excel Solver
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How to implement and solve mathematical optimization models with PuLP in Python
As part of the course you will be get access to case study data, case study descriptions, mathematical model defintions, Excel files, and Python scripts. You can use these as templates for your specific problem.
Requirements for taking this course
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Some basic knowledge of mathematical programming: You should have head about linear optimization before
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Intermediate Python skills: You should know what a list comprehension is, and you should be familiar with common libraries such as NumPy
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Beginner Excel skills: You should be comfortable writing and using formulas, but you do not need to have heard about Excel Solver before; and you will also not need to write any macros etc. at all
