Giorgia Guarnotta
Industrial Vehicles’ Inventory Management using Data-Driven Stochastic Optimization and Forecasting.
Rel. Luca Vassio, Marco Mellia. Politecnico di Torino, Corso di laurea magistrale in Ict For Smart Societies (Ict Per La Società Del Futuro), 2022
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) | Preview |
Abstract
Warehouse optimization is a process that aims at improving the management of time, space, and resources inside a warehouse, minimizing the overall costs yet ensuring a satisfactory quality of service for the customers. When running an efficient warehouse most effort is on the inventory management, which is a well-known challenge for businesses: on the one hand they have to avoid late product supply, which would result in lost profits, while on the other hand they need to focus on a careful inventory control to mitigate the rise of costs, caused by an excessive accumulation of products. The purpose of this thesis is that of supporting industrial fleet managers when vehicles undergo maintenance, providing a way to cope with the need of spare parts under realistic situations, represented by the uncertain nature of components demand.
This work proposes a Two-stage Stochastic Mixed-Integer Nonlinear Problem that aims at minimising both spare parts inventory costs and vehicle offline periods when the requested items are not immediately available
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
URI
![]() |
Modifica (riservato agli operatori) |
