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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Learning from Nature. Using Genetic Algorithms for Inventory Optimisation

Learning from Nature. Using Genetic Algorithms for Inventory Optimisation

Pfeiffer, Leopold

Paperback

General MathematicsProgramming

ISBN10: 3346305007
ISBN13: 9783346305008
Publisher: Grin Verlag
Published: Mar 16 2021
Pages: 64
Weight: 0.21
Height: 0.15 Width: 5.83 Depth: 8.27
Language: English
Bachelor Thesis from the year 2020 in the subject Mathematics - Applied Mathematics, grade: 1,00, University of Augsburg (Quantitative Methods), language: English, 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.

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