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Inventory optimization for Pingo Doce & Go Nova - a deep reinforcement learning approach
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Resumo(s)
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.
Descrição
Palavras-chave
Drl = deep reinforcement learning Ppo = proximal policy optimization Mdp = markov decision process Ml = machine learning Pd&G = Pingo Doce & Go Dqn = deep q- learning Rl = reinforcement learning