Sustainable fuzzy inventory for concurrent fabrication and material depletion modeling with random substandard items
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.4.06Keywords:
Neutrosophic fuzzy number, Python, Sustainability, Depletion, Substandard items, FabricationDimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This study aims to develop a fuzzy inventory model for sustainable concurrent fabrication and material depletion model with randomly selected substandard items. An Economic Production Quantity (EPQ) model was developed using a single-valued neutrosophic number. Substandard items were modeled as random variables. To determine the optimal production strategy, the model was solved numerically using Python’s SciPy library to obtain the production quantity, amount of fabrication, capacity of vehicle, fabrication period, depletion period, preventive measures, duration of vehicle and the total cost. The models parameters were estimated using relevant historical data and industry reports. During the fabrication period the demand is uncertain a single-valued triangular neutrosophic number, Fb = (5,980, 6000, 6500); 0.98, 0.04, 0.03 is used to handle uncertain demand and defuzzification is used to demonstrate the crisp value of demand. A numerical example solved with Python shows a total cost of $235,271.60, offering important insights into the model’s economic implications.Abstract
How to Cite
Downloads
Similar Articles
- Anjali Thapa, Yunus Ali, Sanjay Madan, Pragya Verma, Prajwal Verma, Naveen Gaurav, An Assessment of in vitro Propagation and Medicinal Properties of Datura stramonium (Dhatura) , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- A.P. Asha Sapna, C. Anbalagan, Towards a better living environment-compressive strength and water absorption testing of mini compressed stabilized earth blocks and fired bricks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Jadhav Girish Vasantrao, Chirag Patel, AT&C and non-technical loss reduction in smart grid using smart metering with AI techniques , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Rajesh Kumar Sharma, Amrendra Jha, ECOLOGICAL SCREENING OF SHATIYA WETLAND IN RELATION TO AGRICULTURAL PRODUCTIVITY , The Scientific Temper: Vol. 9 No. 1&2 (2018): The Scientific Temper
- D. Jayadurga, A. Chandrabose, Expanding the quantity of virtual machines utilized within an open-source cloud infrastructure , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Archana Borde, Dattatraya Pandurang Rane, Pratap Vasantrao Pawar, Role of artificial intelligence in digital marketing in enhancing customer engagement , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- Manu Narendra Dev Purohit, Deepika Yadav, Naresh Vyas, Population Studies on Snails , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Theophilus Deenadayal, Tarun Jain, Floristic composition in Paramananda Devara Gudda A sacred grove at Lingadahalli Village Devadurga Taluk Raichur District Karnataka, India , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Krishna Deo Verma, A NOTE ON AGRICULTURE; CONCERNS,OPPORTUNITIES AND CHALLENGES , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- Nithya Raju , Shruthi Deivigarajan, Sindhuja Santhakumar, Sneha Balamurugan, Challenges encountered by healthcare professionals in monitoring adverse events due to medical devices-A review , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 12 13 14 15 16 17 18 19 20 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- P. Janavarthini, Dr. I. Antonitte Vinoline, Green inventory model for growing items with constraints under demand uncertainty , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper

