In this article, we present a novel methodology for inventory management in the pharmaceutical industry, considering the nature of its supply chain. Traditional inventory models often fail to capture the particularities of the pharmaceutical sector, characterized by limited storage space, product degradation, and trade credits. To address these particularities, using fuzzy logic, we propose models that are adaptable to real-world scenarios. The proposed models are designed to reduce total costs for both vendors and clients, a gap not explored in the existing literature. Our methodology employs pentagonal fuzzy number (PFN) arithmetic and Kuhn–Tucker optimization. Additionally, the integration of the naive Bayes (NB) classifier and the use of the Weka artificial intelligence suite increase the effectiveness of our model in complex decision-making environments. A key finding is the high classification accuracy of the model, with the NB classifier correctly categorizing approximately 95.9% of the scenarios, indicating an operational efficiency. This finding is complemented by the model capability to determine the optimal production quantity, considering cost factors related to manufacturing and transportation, which is essential in minimizing overall inventory costs. Our methodology, based on machine learning and fuzzy logic, enhances the inventory management in dynamic sectors like the pharmaceutical industry. While our focus is on a single-product scenario between suppliers and buyers, future research hopes to extend this focus to wider contexts, as epidemic conditions and other applications.

We attempted to synthesize nickel oxide nanoparticles (NiO-NPs) utilizing waste Arachis hypogaea (peanut) shell extract and studied their structural, morphological, and biological performance for biomedical applications. The green engineered NiO-NPs possessed a face-centered cubic structure with an average particle size of 20 nm in highly crystalline form. NiO-NPs were shown to have an optical resonance peak at 327 nm with 3 eV as the optical band gap according to the UV–visible spectra, and the stretching band between Ni–O were evidenced from the FTIR and Raman spectrum. Utilizing green approach the stable nanoparticles were obtained with average particle size of 31 nm from SEM analysis; zeta potential value of −17.6 mV, and PDI as 0.68, revealed the formation of spherical nanoparticles with distinct morphologies without aggregation. XPS analysis confirmed the oxidation states of the elements Ni (2p) and O (1s). This approach may help to increase the surface area, increasing the possibility of nanoparticles interacting with bacterial cells. Furthermore, the presence of nickel and the oxygen oxidation state were confirmed by XPS. Proteus vulgaris, Streptococcus oralis, Bacillus subtilis, and Escherichia coli were found to be susceptible to the antibacterial action of the produced NiO-NPs, with a maximal zone of inhibition of 10.25 mm at 500 μg/ml for P. vulgaris. For P. vulgaris and E. coli, the minimum inhibitory concentrations of NiO were 5.36 and 12.55 %, respectively, at 31.25 μg mL⁻¹. We hereby claim that green engineered NiO NPs decorated with A. hypogaea shell extract have great potential for pharmaceutical and biomedical applications.