Artificial neural networks for predicting the demand and price of the ‎‎hybrid elec‎tric vehicle spare parts

AlAlaween, Wafa’ H.; Abueed, Omar A.; Alalawin, Abdallah; Abdallah, Omar H.; Albashabsheh, Nibal T.; Abdelall, Esraa S.; Al-Abdallat, Yousef A.

doi:10.1080/23311916.2022.2075075

Cited by 5 publications

(1 citation statement)

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“…They proposed a new hybrid method using Lagrangian relaxation algorithm to solve a multi-objective nonlinear programming (NLP) model. AlAlaween et al [34] proposed an artificial neural network to forecast the uncertainty of the demand and price in the hyrrid electric cars' spare parts.…”

Section: Introductionmentioning

confidence: 99%

Backordering policy for hi-tech products with price and demand uncertainty during the pandemics

Yu,

Huang,

Chen

et al. 2024

Scientia Iranica

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The decline phase of a high-tech product's life cycle often causes variations in production schedules due to over or under production. To address this challenge, we propose a production-inventory model that considers decreasing demand and prices during pandemics. Our model aims to optimize replenishment, dispatching, and backordering policies, ultimately maximizing total profit for high-tech industries. Our proposed solution procedure derives 2 optimal policies, taking into account the unique demands during the pandemics. We suggest adopting either a last-in-first-out (LIFO) or first-in-first-out (FIFO) backordering policy depending on whether profit or fairness is the primary concern. We have provided a numerical example and conducted a sensitivity analysis to demonstrate the practical application of the proposed model. By optimizing the replenishment, our model enables high-tech industries to maximize total profit, even in the face of declining demand and prices during the pandemics. Overall, our model represents a valuable tool for high-tech industries seeking to better manage production and inventory, and we believe it has the potential to increase product profitability during the declining phase of a product's life cycle.

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Section: Introductionmentioning

confidence: 99%