Effect of Uncertainty in Optimal Inventory Policy for Manufacturing Products
Keywords:
Inventory system, Fuzzy, Signed distance method, variable demandAbstract
To provide the best storage facilities for an items is one of the main part for any inventory management system. There are many categories for perishable items which deteriorates at various rates due to temperature and some other environment conditions. This research study developed a fuzzy inventory model by considering the time varying demand. The model incorporates the linear decreasing demand with signed distance method. In inventory system the reliability of any procedure is the significant property in research work in which some parameters are very difficult to assign the values or nearly unreal. Fuzzy inventory models are quite useful in practice in such cases. The effectiveness of this system is shown through the consequence of fuzzy parameters on total inventory cost were considered, and a new improved model was modified and also for demonstrated the relationship between crisp and fuzzy environment.
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References
B. Liu, K. Iwamura (1998). A note on chance constrained programming with fuzzy coefficients, Fuzzy sets and Systems, 100, 229-233.
Bellman, R. E. and Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17, 141-164.
C. C. Chou (2009). Fuzzy economic order quantity inventory model, International Journal of Innovative Computing, Information and Control, 5(9), 2585-2592.
C. Kao and W. K. Hsu (2002). Lot size-reorder point inventory model with fuzzy demands, Computers and Mathematics with Applications, vol.43, pp.1291-1302.
Chang, H., C., Yao, J., S., and Quyang, L.Y., (2006). Fuzzy mixture inventory model involving fuzzy random variable, lead-time and fuzzy total demand. European Journal of Operational Research, 69, 65-80.
Daniel Cardoso de Salles, Armando Celestino Gonalves Neto and Lino Guimaraes Marujo (2016). Using fuzzy logic to implement decision policies in system dynamics models, Expert Systems with Applications, 55, 172-183.
Dutta, P., Chakraborty, D., and Roy, A.R., (2007). Continuous review inventory model in mixed fuzzy and stochastic environment. Applied Mathematics and Computation, 188, 970-980.
Guiffrida, A.L. (2010). “Fuzzy inventory models” in: Inventory Management: Non Classical Views, (Chapter 8). M.Y. Jaber (Ed.), CRC Press, FL, Boca Raton, 173-190.
Gupta, K. K., Sharma, A., Singh, P. R., Malik, A. K. (2013). Optimal ordering policy for stock-dependent demand inventory model with non-instantaneous deteriorating items. International Journal of Soft Computing and Engineering, 3(1), 279-281.
H. C. Chang, J. S. Yao and L. Y. Ouyang (2004). Fuzzy mixture inventory model with variable lead-time based on probabilistic fuzzy set and triangular fuzzy number, Mathematical and Computer Modeling, vol.29, pp.387-404, 2004
H. J. Zimmermann (1976). “Description & optimization of fuzzy systems,” International Journal of General Systems, 2(4) 209–215.
H. J. Zimmermann (1985). Fuzzy Set Theory and Its Applications. Kluwer-Nijho, Hinghum, Netherlands.
Hadley, G., Whitin T. M., (1963). Analysis of inventory system, Prentice-Hall, Englewood clipps, NJ.
Halim, K.A., Giri, B.C. & Chaudhuri, K.S. (2010). Lot sizing in an unreliable manufacturing system with fuzzy demand and repair time. International Journal of Industrial and Systems Engineering, 5, 485-500.
Harris, F., (1915). Operations and cost, A W Shaw Co. Chicago.
Hollah, O.M., Fergany, H.A. (2019). Periodic review inventory model for Gumbel deteriorating items when demand follows Pareto distribution. J Egypt Math Soc 27, 10, https://doi.org/10.1186/s42787-019-0007-z.
Hsieh, C.H. (2002). Optimization of fuzzy production inventory models. Information Sciences, 146, 29-40.
J. S. Yao and J. Chiang, (2003). Inventory without back order with fuzzy total cost and fuzzy storing cost deffuzified by centroid and singed distance, European Journal of Operational Research, 148, 401-409.
Jaggi K. et al., (2013). Fuzzy inventory model for deteriorating items with time-varying demand and shortages. American Journal of Operational Research. 2(6), 81-92.
K. S. Park, (1987). Fuzzy set theoretic interpretation of economic order quantity, IIIE Transactions on Systems, Man and Cybernetics, 17, 1082-1084.
Kumar, S., Chakraborty, D., Malik, A. K. (2017). A Two Warehouse Inventory Model with Stock-Dependent Demand and variable deterioration rate. International Journal of Future Revolution in Computer Science & Communication Engineering, 3(9), 20-24.
Kumar, S., Malik, A. K., Sharma, A., Yadav, S. K., Singh, Y. (2016, March). An inventory model with linear holding cost and stock-dependent demand for non-instantaneous deteriorating items. In AIP Conference Proceedings (Vol. 1715, No. 1, p. 020058). AIP Publishing LLC.
Kumar, S., Soni, R., Malik, A. K. (2019). Variable demand rate and sales revenue cost inventory model for non-instantaneous decaying items with maximum life time. International Journal of Engineering & Science Research, 9(2), 52-57.
Malik, A. K. and Singh, Y. (2011). An inventory model for deteriorating items with soft computing techniques and variable demand. International Journal of Soft Computing and Engineering, 1(5), 317-321.
Malik, A. K. and Singh, Y. (2013). A fuzzy mixture two warehouse inventory model with linear demand. International Journal of Application or Innovation in Engineering and Management, 2(2), 180-186.
Malik, A. K., Chakraborty, D., Bansal, K. K., Kumar, S. (2017). Inventory Model with Quadratic Demand under the Two Warehouse Management System. International Journal of Engineering and Technology, 9(3), 2299-2303.
Malik, A. K., Mathur, P., Kumar, S. (2019, August). Analysis of an inventory model with both the time dependent holding and sales revenue cost. In IOP Conference Series: Materials Science and Engineering (Vol. 594, No. 1, p. 012043). IOP Publishing.
Malik, A. K., Shekhar, C., Vashisth, V., Chaudhary, A. K., Singh, S. R. (2016, March). Sensitivity analysis of an inventory model with non-instantaneous and time-varying deteriorating Items. In AIP Conference Proceedings (Vol. 1715, No. 1, p. 020059). AIP Publishing LLC.
Malik, A. K., Singh, S. R., Gupta, C. B. (2008). An inventory model for deteriorating items under FIFO dispatching policy with two warehouse and time dependent demand. Ganita Sandesh, 22(1), 47-62.
Malik, A. K., Singh, Y., Gupta, S. K. (2012). A fuzzy based two warehouses inventory model for deteriorating items. International Journal of Soft Computing and Engineering, 2(2), 188-192.
Malik, A. K., Vedi, P., and Kumar, S. (2018). An inventory model with time varying demand for non-instantaneous deteriorating items with maximum life time. International Journal of Applied Engineering Research, 13(9), 7162-7167.
Malik, A.K. and Garg, H. (2021). An Improved Fuzzy Inventory Model Under Two Warehouses. Journal of Artificial Intelligence and Systems, 3, 115–129. https://doi.org/10.33969/AIS.2021.31008.
Malik, A.K. and Sharma, A. (2011). An Inventory Model for Deteriorating Items with Multi-Variate Demand and Partial Backlogging Under Inflation, International Journal of Mathematical Sciences, 10(3-4), 315-321.
Malik, A.K., Singh, A., Jit, S., Garg. C.P. (2010). “Supply Chain Management: An Overview”. International Journal of Logistics and Supply Chain Management, 2(2), 97-101.
Priyan S., Manivannan P., (2017). Optimal inventory modelling of supply chain system involving quality inspection errors and fuzzy effective rate, Opsearch. 54, 21-43.
Sarkar, B., and Mahapatra, A.S., (2017). Periodic review fuzzy inventory model with variable lead time and fuzzy demand, International Transactions in Operational Research, 24, 11971227.
Sharma, A., Gupta, K. K., Malik, A. K. (2013). Non-Instantaneous Deterioration Inventory Model with inflation and stock-dependent demand. International Journal of Computer Applications, 67(25), 6-9.
Shekarian, E., Kazemi, N., Abdul-Rashid, S.H., and Olugu, E.U. (2017). Fuzzy inventory models: A comprehensive review, Applied Soft Computing, 55, 588-621.
Jang Bahadur, D. K. ., and L. . Lakshmanan. “Virtual Infrastructure Based Routing Algorithm for IoT Enabled Wireless Sensor Networks With Mobile Gateway”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 8, Aug. 2022, pp. 96-103, doi:10.17762/ijritcc.v10i8.5681.
Singh, S. R. and Malik, A. K. (2008). Effect of inflation on two warehouse production inventory systems with exponential demand and variable deterioration. International Journal of Mathematical and Applications, 2(1-2), 141-149.
M. J. Traum, J. Fiorentine. (2021). Rapid Evaluation On-Line Assessment of Student Learning Gains for Just-In-Time Course Modification. Journal of Online Engineering Education, 12(1), 06–13. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/45
Singh, S. R. and Malik, A. K. (2009). Two warehouses model with inflation induced demand under the credit period. International Journal of Applied Mathematical Analysis and Applications, 4(1), 59-70.
Singh, S. R., A. K. Malik (2010). Inventory system for decaying items with variable holding cost and two shops, International Journal of Mathematical Sciences, Vol. 9(3-4), 489-511.
Singh, S. R., Malik, A. K. (2011). An Inventory Model with Stock-Dependent Demand with Two Storages Capacity for Non-Instantaneous Deteriorating Items. International Journal of Mathematical Sciences and Applications, 1(3), 1255-1259.
Singh, S. R., Malik, A. K., & Gupta, S. K. (2011). Two Warehouses Inventory Model for Non-Instantaneous Deteriorating Items with Stock-Dependent Demand. International Transactions in Applied Sciences, 3(4), 911-920.
Singh, Y., Arya, K., Malik, A. K. (2014). Inventory control with soft computing techniques. International Journal of Innovative Technology and Exploring Engineering, 3(8), 80-82.
Paithane, P. M., & Kakarwal, D. (2022). Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 98–104. https://doi.org/10.18201/ijisae.2022.272
Singh, Y., Malik, A. K., Kumar, S., (2014). An inflation induced stock-dependent demand inventory model with permissible delay in payment. International Journal of Computer Applications, 96(25), 14-18.
Sujit Kumar De (2021). Solving an EOQ model under fuzzy reasoning, Applied Soft Computing, 99, 106892, https://doi.org/10.1016/j.asoc.2020.106892.
Tyagi, T., Kumar, S., Malik, A. K., & Vashisth, V. (2022a). A Novel Neuro-Optimization Technique for Inventory Models in Manufacturing Sectors. Journal of Computational and Cognitive Engineering.
Tyagi, T., Kumar, S., Naik, P. J., Kumar, P., & Malik, A. K. (2022b). Analysis of Optimization Techniques in Inventory and Supply Chain Management for Manufacturing Sectors. Journal of Positive School Psychology, 6(2), 5498-5505.
Vashisth, V., Tomar, A., Chandra, S., Malik, A. K. (2016). A trade credit inventory model with multivariate demand for non-instantaneous decaying products. Indian Journal of Science and Technology, 9(15), 1-6.
Yadav, P. ., S. . Kumar, and D. K. J. . Saini. “A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 8, Aug. 2022, pp. 110-5, doi:10.17762/ijritcc.v10i8.5683.
Vashisth, V., Tomar, A., Soni, R., Malik, A. K. (2015). An inventory model for maximum life time products under the Price and Stock Dependent Demand Rate. International Journal of Computer Applications, 132(15), 32-36.
Vujosevic, M. and Petrovic, D. (1996). EOQ formula when inventory cost is fuzzy, International Journal of Production Economics, 45(1996), 499-504.
Yadav, S.R. and Malik, A.K. (2014). Operations Research, Oxford University Press, New Delhi.
Yao J.S. and Lee H.M., (1999). Fuzzy inventory with or without backorder for fuzzy order quantity with trapezoidal fuzzy number. Fuzzy Sets and Systems, 105, 311-337.
Yong He, Shou-Yang Wang, K.K. Lai (2010). An optimal production inventory model for deteriorating items with multiple-market demand European Journal of Operational Research, Volume 203, Issue 3, Pages 593-600.
Gill, D. R. . (2022). A Study of Framework of Behavioural Driven Development: Methodologies, Advantages, and Challenges. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 09–12. https://doi.org/10.17762/ijfrcsce.v8i2.2068
Yung, K. L., W. Ip and D. Wang (2007). Soft Computing Based Procurement Planning of Time-variable Demand in Manufacturing System. International Journal of Automation and Computing, 04(1), 80-87.
Zadeh (1965). Fuzzy sets, Information and Control, 8(3), 338–353.
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