We introduce a novel heuristic designed to address the supply chain inventory management problem in the context of a two-echelon divergent supply chain. The proposed heuristic advances the current state-of-the-art by combining deep reinforcement learning with multi-stage stochastic programming. In particular, deep reinforcement learning is employed to determine the number of batches to produce, while multi-stage stochastic programming is applied to make shipping decisions. To support further research, we release a publicly available software environment that simulates a wide range of two-echelon divergent supply chain settings, allowing the manipulation of various parameter values, including those associated with seasonal demands. We then present a comprehensive set of numerical experiments considering constraints on production and warehouse capacities under fixed and variable logistic costs. The results demonstrate that the proposed heuristic significantly and consistently outperforms pure deep reinforcement learning algorithms in minimizing total costs. Moreover, it overcomes several inherent limitations of multi-stage stochastic programming models, thus underscoring its potential advantages in addressing complex supply chain scenarios.

Stranieri, F., Fadda, E., Stella, F. (2024). Combining deep reinforcement learning and multi-stage stochastic programming to address the supply chain inventory management problem. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 268(February 2024) [10.1016/j.ijpe.2023.109099].

Combining deep reinforcement learning and multi-stage stochastic programming to address the supply chain inventory management problem

Stranieri, F
;
Stella, F
2024

Abstract

We introduce a novel heuristic designed to address the supply chain inventory management problem in the context of a two-echelon divergent supply chain. The proposed heuristic advances the current state-of-the-art by combining deep reinforcement learning with multi-stage stochastic programming. In particular, deep reinforcement learning is employed to determine the number of batches to produce, while multi-stage stochastic programming is applied to make shipping decisions. To support further research, we release a publicly available software environment that simulates a wide range of two-echelon divergent supply chain settings, allowing the manipulation of various parameter values, including those associated with seasonal demands. We then present a comprehensive set of numerical experiments considering constraints on production and warehouse capacities under fixed and variable logistic costs. The results demonstrate that the proposed heuristic significantly and consistently outperforms pure deep reinforcement learning algorithms in minimizing total costs. Moreover, it overcomes several inherent limitations of multi-stage stochastic programming models, thus underscoring its potential advantages in addressing complex supply chain scenarios.
Articolo in rivista - Articolo scientifico
Deep reinforcement learning; Inventory management; Stochastic programming;
English
15-nov-2023
2024
268
February 2024
109099
open
Stranieri, F., Fadda, E., Stella, F. (2024). Combining deep reinforcement learning and multi-stage stochastic programming to address the supply chain inventory management problem. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 268(February 2024) [10.1016/j.ijpe.2023.109099].
File in questo prodotto:
File Dimensione Formato  
Stranieri-2023-Int J of Prod Econ-VoR.pdf

accesso aperto

Descrizione: CC BY 4.0 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Licenza: Creative Commons
Dimensione 955.93 kB
Formato Adobe PDF
955.93 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/453161
Citazioni
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 2
Social impact