Review of Inventory Control Models: A Classification Based on Methods of Obtaining Optimal Control Parameters

Jackson, Ilya; Tolujevs, Jurijs; Kegenbekov, Zhandos

doi:10.2478/ttj-2020-0015

Cited by 16 publications

(10 citation statements)

References 59 publications

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“…Inventory management is well-established in the operations management literature. The existing studies are mostly focused on quantitative models, for example, inventory lot-sizing problems [13], capacity and inventory management [14], inventory control models [15], and inventory systems with deterioration [16]. Limited attention has been given to the issue of excess (over) inventory.…”

Section: Introductionmentioning

confidence: 99%

Destruction Decisions for Managing Excess Inventory in E-Commerce Logistics

Pourhejazy

2020

Sustainability

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The Internet has brought about new possibilities for innovation and radically changed business activities. Internet shopping is a prime example of increasing popularity, which is exacerbated due to the recent pandemic. It is expected that e-commerce will accommodate more than a quarter of the total retail sales worldwide in the next few years. Given the characteristics of e-commerce, inventory management is of paramount importance for an effective and timely response to the online customers’ demand. Despite its relevance, the issue of warehouse excess inventory is not sufficiently studied in the operations management literature. This study explores the factors, including sustainability and strategic considerations, that influence the inventory destruction decisions as one of the alternatives for managing excess inventory. Applying the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, the interrelationships between the decision factors are investigated and the decisive considerations are identified. Overall, the outcomes provide insights for the e-commerce practitioners and offer directions for modeling and managing inventory destruction decisions.

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Section: Introductionmentioning

confidence: 99%

Destruction Decisions for Managing Excess Inventory in E-Commerce Logistics

Pourhejazy

2020

Sustainability

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“…The tendency of the DRL agent to exploit simulation mechanics has been discovered in this study and deserves primary consideration in development and validation. Therefore, it is crucial to highlight that many advanced supply chain models exist as discrete-event simulation models [41]. Nowadays, the vast majority of DRL research follows the OpenAI Gym standard [42].…”

Section: Discussionmentioning

confidence: 99%

Deep Reinforcement Learning for Supply Chain Synchronization

Jackson¹

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Supply chain synchronization can prevent the "bullwhip effect" and significantly mitigate ripple effects caused by operational failures. This paper demonstrates how deep reinforcement learning agents based on the proximal policy optimization algorithm can synchronize inbound and outbound flows if end-toend visibility is provided. The paper concludes that the proposed solution has the potential to perform adaptive control in complex supply chains. Furthermore, the proposed approach is general, task unspecific, and adaptive in the sense that prior knowledge about the system is not required.

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“…The tendency of the learning agent to abuse and exploit simulation mechanics has been discovered in this research and deserves primary consideration in development and deployment. It is also worth emphasizing that a manifold of advanced supply chain models exists in the form of discrete event simulations (DES) [28]. Currently, the vast majority of RL research follows the standard prescribed by the OpenAI Gym framework [29].…”

Section: Discussionmentioning

confidence: 99%

Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning

Kegenbekov

Jackson

2021

Algorithms

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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.

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Review of Inventory Control Models: A Classification Based on Methods of Obtaining Optimal Control Parameters

Cited by 16 publications

References 59 publications

Destruction Decisions for Managing Excess Inventory in E-Commerce Logistics

Destruction Decisions for Managing Excess Inventory in E-Commerce Logistics

Deep Reinforcement Learning for Supply Chain Synchronization

Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning

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