Publicação
Inventory optimization for Pingo Doce & Go Nova - a deep reinforcement learning approach
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Han, Qiwei | |
| dc.contributor.author | Belchior, Rodrigo Castelo Branco | |
| dc.date.accessioned | 2024-11-11T13:58:13Z | |
| dc.date.available | 2024-11-11T13:58:13Z | |
| dc.date.issued | 2024-02-01 | |
| dc.date.submitted | 2024-02-27 | |
| dc.description.abstract | Addressing PD&G’s inventory management inefficiencies, this research evaluates the in tegration of predictive analytics and deep reinforcement learning (DRL) to navigate the store’s unique demand influenced by the academic environment. Traditional MRP sys tems’ shortcomings are addressed by implementing SARIMAX, XGBoost, and Neural Prophet models for demand forecasting, alongside DQN and PPO for stock replenish ment optimization. Results demonstrate that advanced forecasting models and DRL may greatly enhance the accuracy of inventory management in comparison to the currently used practices. The deployment of these sophisticated models not only enhances PD&G’s operational efficiency but also pioneers innovative practices in retail inventory management. | pt_PT |
| dc.identifier.tid | 203605608 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/174968 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | UID/ECO/00124/2013 | pt_PT |
| dc.subject | Drl = deep reinforcement learning | pt_PT |
| dc.subject | Ppo = proximal policy optimization | pt_PT |
| dc.subject | Mdp = markov decision process | pt_PT |
| dc.subject | Ml = machine learning | pt_PT |
| dc.subject | Pd&G = Pingo Doce & Go | pt_PT |
| dc.subject | Dqn = deep q- learning | pt_PT |
| dc.subject | Rl = reinforcement learning | pt_PT |
| dc.title | Inventory optimization for Pingo Doce & Go Nova - a deep reinforcement learning approach | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics. | pt_PT |