Learning from Nature. Using Genetic Algorithms for Inventory Optimisation

Learning from Nature. Using Genetic Algorithms for Inventory Optimisation

1. Auflage

von: Leopold Pfeiffer

16,99 €

Verlag: Grin Verlag
Format: PDF
Veröffentl.: 27.11.2020
ISBN/EAN: 9783346304995
Sprache: englisch
Anzahl Seiten: 58

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Bachelor Thesis from the year 2020 in the subject Mathematics - Applied Mathematics, grade: 1,00, University of Augsburg (Quantitative Methods), language: English, abstract: Abstract:

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.

Table of Contents

1 Introduction
2 Literature Review
2.1 Literature on Serial Supply Chains
2.2 Literature on Genetic Algorithms
3 Description of the Serial Supply Chain Model
3.1 Model Assumptions
3.2 Mathematical Formulation
4 Theory of Genetic Algorithms
4.1 Optimisation Problem and Solution Representation
4.2 Iterative Process
4.3 Caveats and Limitations
4.3.1 Parameter Tuning
4.3.2 Multimodality
4.3.3 Convergence to the Global Optimum
5 Implementing a GA for Base-Stock Level Optimisation
5.1 Supply Chain Model Implementation
5.2 GA Implementation
5.2.1 Parameters
5.2.2 Iterative Steps
6 Empirical Testing
6.1 Parameter Tuning
6.2 Simulation Runs
7 Conclusion
8 Bibliography
9 Appendices

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