Online reinforcement learning-based inventory control for intelligent E-Fulfilment dealing with nonstationary demand

Mo, Daniel Y.; Tsang, Y.P.; Wang, Y.; Xu, Weikun

doi:10.1080/17517575.2023.2284427

Cited by 3 publications

(1 citation statement)

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“…The next subfield of artificial intelligence, which has also been widely implemented to address inventory control problems, is reinforcement learning. Numerous prior studies have explored the utilization of reinforcement learning (RL) in addressing inventory management challenges, exemplified by the noteworthy contributions [24][25][26][27]. For example, in reference [24], a deep reinforcement learning framework was introduced for inventory replenishment problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Network Control of Perishable Inventory with Fixed Shelf Life Products and Fuzzy Order Refinement under Time-Varying Uncertain Demand

Chołodowicz,

Orłowski

2024

Energies

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Many control algorithms have been applied to manage the flow of products in supply chains. However, in the era of thriving globalization, even a small disruption can be fatal for some companies. On the other hand, the rising environmental impact of a rapid industry is imposing limitations on energy usage and waste generation. Therefore, taking into account the mentioned perspectives, there is a need to explore the research directions that concern product perishability together with different demand patterns and their uncertain character. This study aims to propose a robust control approach that combines neural networks and optimal controller tuning with the use of both different demand patterns and fuzzy logic. Firstly, the demand forecast is generated, following which the parameters of the neural controller are optimized, taking into account the different demand patterns and uncertainty. As part of the verification of the designated controller, the sensitivity to parameter changes has been determined using the OAT method. It turns out that the proposed approach can provide significant waste reductions compared to the well-known POUT method while maintaining low stocks, a high fill rate, and providing lower sensitivity for parameter changes in most considered cases. The effectiveness of this approach is verified by using a dataset from a worldwide retailer. The simulation results show that the proposed approach can effectively improve the control of uncertain perishable inventories.

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