A model for decision-making to parameterizing Demand Driven Material Requirement Planning using deep reinforcement learning

• Autorzy: El Marzougui Mustapha , Messaoudi Najat , Dachry Wafaa , Bahloul Bensassi

Identyfikatory

DOI

10.30657/pea.2024.30.37

• Języki publikacji: EN

Abstrakty

EN

Demand-Driven Material Requirements Planning (DDMRP) is an emerging inventory management approach that has garnered significant attention from academia and industry. Numerous recent studies have highlighted the advantages of DDMRP compared to traditional methods such as material requirement planning (MRP), Theory of constraint (TOC), and Kanban. However, the performance of DDMRP relies on several parameters that affect its effectiveness. Parameterization models and the optimization of control variables have significantly contributed to the field of inventory management and have proven to be effective and practical in addressing challenges by providing a structured approach to handling complex variables and constraints. This paper introduces an innovative parameterization model that leverages deep reinforcement learning (DRL) to parameterize a DDMRP system in the face of uncertain demand. The main objective is to dynamically determine the optimal values for the variability and lead time factors within the DDMRP framework, to maximize customer service levels and optimize inventory efficiency. The results of this study emphasize the effectiveness of DRL as an automated decision-making approach for controlling DDMRP parameters. Additionally, the findings highlight the potential for enhancing the performance of the DDMRP approach, particularly in terms of on-time delivery (OTD) and average on-hand inventory (AOHI) by adjusting the variability and lead-time factors.

Słowa kluczowe

EN

DDMRP; Deep Reinforcement Learning; Parameterization; on-hand inventory; on-time delivery

PL

DDMRP; głębokie zbrojenie; uczenie się; parametryzacja; terminowość dostaw; stany magazynowe

• Wydawca: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
• Czasopismo: Production Engineering Archives
• Rocznik: 2024
• Tom: Vol. 30, Iss. 3
• Strony: 377--393
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

El Marzougui Mustapha

• Laboratory of Electrical and Industrial Engineering, Information Processing, Computing and Logistics, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco

• https://orcid.org/0009-0007-0834-1669

autor

Messaoudi Najat

• Laboratory of Electrical and Industrial Engineering, Information Processing, Computing and Logistics, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco

• https://orcid.org/0000-0002-2554-2020

autor

Dachry Wafaa

• Laboratory of Electrical and Industrial Engineering, Information Processing, Computing and Logistics, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco
• Laboratory of Engineering, Industrial Management and Innovation, FST, Hassan I University, Settat, Morocco

• https://orcid.org/0009-0006-0489-1017

autor

Bahloul Bensassi

• Laboratory of Electrical and Industrial Engineering, Information Processing, Computing and Logistics, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco

• https://orcid.org/0000-0003-4129-4247

Bibliografia

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• 2. Bahu, B., Bironneau, L., Hovelaque, V., 2019. Compréhension du DDMRP et de son adoption : premiers éléments empiriques. Logistique & Management, 27(1), 20–32. DOI: 10.1080/12507970.2018.1547130
• 3. Benavente, D., Peralta, S., Quispe, G., Moguerza, J., Raymundo, C., 2023. The Demand Driven MRP Implementation in Complex Manufacturing Industries: A Systematic Literature Reviews. International Journal of Engineering Trends and Technology, 71(3), 33–45. DOI: 10.14445/22315381/IJETT-V71I3P205
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).