Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning
Adaptive and highly synchronized supply chains can avoid a cascading rise-and-fall inventory dynamic and mitigate ripple effects caused by operational failures. This paper aims to demonstrate how a deep reinforcement learning agent based on the proximal policy optimization algorithm can synchronize...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/14/8/240 |
| _version_ | 1797524985627017216 |
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| author | Zhandos Kegenbekov Ilya Jackson |
| author_facet | Zhandos Kegenbekov Ilya Jackson |
| author_sort | Zhandos Kegenbekov |
| collection | DOAJ |
| description | Adaptive and highly synchronized supply chains can avoid a cascading rise-and-fall inventory dynamic and mitigate ripple effects caused by operational failures. This paper aims to demonstrate how a deep reinforcement learning agent based on the proximal policy optimization algorithm can synchronize inbound and outbound flows and support business continuity operating in the stochastic and nonstationary environment if end-to-end visibility is provided. The deep reinforcement learning agent is built upon the Proximal Policy Optimization algorithm, which does not require hardcoded action space and exhaustive hyperparameter tuning. These features, complimented with a straightforward supply chain environment, give rise to a general and task unspecific approach to adaptive control in multi-echelon supply chains. The proposed approach is compared with the base-stock policy, a well-known method in classic operations research and inventory control theory. The base-stock policy is prevalent in continuous-review inventory systems. The paper concludes with the statement that the proposed solution can perform adaptive control in complex supply chains. The paper also postulates fully fledged supply chain digital twins as a necessary infrastructural condition for scalable real-world applications. |
| first_indexed | 2024-03-10T09:05:23Z |
| format | Article |
| id | doaj.art-80f5c10f3938457195b106d4eb07db63 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T09:05:23Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-80f5c10f3938457195b106d4eb07db632023-11-22T06:27:47ZengMDPI AGAlgorithms1999-48932021-08-0114824010.3390/a14080240Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement LearningZhandos Kegenbekov0Ilya Jackson1Faculty of Engineering and Information Technology, Kazakh-German University, Pushkin 111, Almaty 050010, KazakhstanCenter for Transportation & Logistics, Massachusetts Institute of Technology, 1 Amherst Street, Cambridge, MA 02142, USAAdaptive and highly synchronized supply chains can avoid a cascading rise-and-fall inventory dynamic and mitigate ripple effects caused by operational failures. This paper aims to demonstrate how a deep reinforcement learning agent based on the proximal policy optimization algorithm can synchronize inbound and outbound flows and support business continuity operating in the stochastic and nonstationary environment if end-to-end visibility is provided. The deep reinforcement learning agent is built upon the Proximal Policy Optimization algorithm, which does not require hardcoded action space and exhaustive hyperparameter tuning. These features, complimented with a straightforward supply chain environment, give rise to a general and task unspecific approach to adaptive control in multi-echelon supply chains. The proposed approach is compared with the base-stock policy, a well-known method in classic operations research and inventory control theory. The base-stock policy is prevalent in continuous-review inventory systems. The paper concludes with the statement that the proposed solution can perform adaptive control in complex supply chains. The paper also postulates fully fledged supply chain digital twins as a necessary infrastructural condition for scalable real-world applications.https://www.mdpi.com/1999-4893/14/8/240deep reinforcement learningproximal policy optimizationsupply chains |
| spellingShingle | Zhandos Kegenbekov Ilya Jackson Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning Algorithms deep reinforcement learning proximal policy optimization supply chains |
| title | Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning |
| title_full | Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning |
| title_fullStr | Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning |
| title_full_unstemmed | Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning |
| title_short | Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning |
| title_sort | adaptive supply chain demand supply synchronization using deep reinforcement learning |
| topic | deep reinforcement learning proximal policy optimization supply chains |
| url | https://www.mdpi.com/1999-4893/14/8/240 |
| work_keys_str_mv | AT zhandoskegenbekov adaptivesupplychaindemandsupplysynchronizationusingdeepreinforcementlearning AT ilyajackson adaptivesupplychaindemandsupplysynchronizationusingdeepreinforcementlearning |