Optimizing machine changeovers in Excel and Python

[mitsumi]

Optimizing machine changeovers in Excel and Python
Published 11/2023
Created by Linnart Sining Felkl
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 12 Lectures ( 2h 20m ) | Size: 866 MB

An practical introduction to mathematical programming in Excel and Python

What you'll learn
How to use Excel Solver in Excel for mathematical programming
How to use Python for mathematical programming
Define a changeover matrix for a machine
Define changeover sequencing problem as mathematical optimization problem
Implement a single machine changeover sequence optimization problem in Excel Solver
Implement a single machine changeover sequence optimization problem in Python
Requirements
Basic knowledge of linear programming required
Basic knowledge of mathematical programming required
Basic Python knowledge required
Basic Excel knowledge required
Description
This course provides a practical introduction to mathematical modeling and optimization in Excel and Python, usingExcel Solver in Excel PuLP and default free solvers through PuLP in PythonA 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:Optimal setup and changeover sequence for a one-time production programOptimal changeover sequence for a repeated production cycle, i.e. repetitive cyclic production programAs part of this course, you will see and learnHow to formally define a changeover sequencing problem mathematicallyGet an overview of modeling frameworks and solvers in Excel and PythonHow to setup Excel Solver and how to implement mathematical models with Excel Solver How to implement and solve mathematical optimization models with PuLP in PythonAs 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 courseSome basic knowledge of mathematical programming: You should have head about linear optimization beforeIntermediate Python skills: You should know what a list comprehension is, and you should be familiar with common libraries such as NumPyBeginner 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
Who this course is for
Production planners
Excel and Python learners
Students of majors such as operations research, industrial engineering, production management, or similar

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