Learning from Nature. Using Genetic Algorithms for Inventory Optimisation (eBook, PDF)
Sofort per Download lieferbar
Statt: 27,95 €**
18,99 €
inkl. MwSt. und vom Verlag festgesetzt.
**Preis der gedruckten Ausgabe (Broschiertes Buch)
Alle Infos zum eBook verschenkenWeitere Ausgaben:
PAYBACK Punkte
0 °P sammeln!
**Preis der gedruckten Ausgabe (Broschiertes Buch)
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 extens...
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.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Andere Kunden interessierten sich für
