Learning from Nature. Using Genetic Algorithms for Inventory Optimisation


Bachelor Thesis, 2020

58 Pages, Grade: 1,00


Abstract or Introduction

A battery of approaches has been applied by researchers and practitioners in the field of inventory optimisation to find optimal inventory policies that can drive the success of businesses of various industries. One such approach is based on the use of genetic algorithms, a multi-purpose subclass of evolutionary algorithms that imitate the prin- ciples of evolution to solve combinatorial problems. In this thesis, we extensively explore the theoretical background of inventory optimisation as well as genetic algorithms before we develop a four-stage serial supply chain model and implement a genetic algorithm for base-stock level optimisation.

Details

Title
Learning from Nature. Using Genetic Algorithms for Inventory Optimisation
College
University of Augsburg  (Quantitative Methods)
Grade
1,00
Author
Year
2020
Pages
58
Catalog Number
V958671
ISBN (eBook)
9783346304995
ISBN (Book)
9783346305008
Language
English
Keywords
Genetic Algorithms, Supply Chain, Inventory, Optimisation
Quote paper
Leopold Pfeiffer (Author), 2020, Learning from Nature. Using Genetic Algorithms for Inventory Optimisation, Munich, GRIN Verlag, https://www.grin.com/document/958671

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