An EOQ Model for Defective Items Under Pythagorean Fuzzy Environment
محورهای موضوعی : Business Administration and Entrepreneurship
Mitali Nayak
1
,
Suchitra Patnaik
2
,
Milu Acharya
3
1 - Department of Mathematics, ITER
Siksha O Anusandhan, Jagmara, Khandagiri, Bhubaneswar, Odisha
2 - Department of Mathematics, ITER
Siksha O Anusandhan, Jagmara, Khandagiri
3 - Department of Mathematics, ITER
Siksha O Anusandhan, Jagmara, Khandagiri
کلید واژه: Pythagorean Fuzzy Set, EOQ inventory system, scores function, Yager’s ranking function,
چکیده مقاله :
Classical EOQ models can help us decide on the terms of how much to order, to manage an inventory. Any company dealing with physical products needs to manage an inventory to improve and avoid shortages occurring. Many times the lots come with defective items due to which there is a loss in the effectiveness of the model. In the present study, we consider two types of carrying costs for good and defectives items, and also proportionate discount is being considered for defective items. We use the Pythagorean fuzzy environment (PFS) and analyze the score functions with help of cuts of the fuzzy parameters. The problem is optimized to get the best solution, utilizing Yager’s Ranking. The numerical results obtained from crisp and fuzzy environments are also compared. Lastly graphical and sensitivity illustrations are being used to justify the models.
Atanassov, K.T.(1986) ‘Intuitionistic fuzzy sets, Fuzzy sets and Systems’ Vol.20(1), pp.87-96
Ahmadi, A., Mohabbatdar, S., & Sajadieh, M. S. (2016). Optimal manufacturer-retailer policies in a supply chain with defective products and price-dependent demand. Journal of Optimization in Industrial Engineering, 9(19), 37-46.
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B-141.
Chang, H. C. (2004). An application of fuzzy sets theory to the EOQ model with imperfect quality items. Computers & Operations Research, 31(12), 2079-2092.
Chen, S. H., & Chang, S. M. (2008). Optimization of fuzzy production inventory model with unrepairable defective products. International Journal of Production Economics, 113(2), 887-894.
DE, S. K., & Mahata, G. C. (2019). A comprehensive study of an economic order quantity model under fuzzy monsoon demand. Sādhanā, 44(4), 1-12.
De, S. K. (2021). Solving an EOQ model under fuzzy reasoning. Applied Soft Computing, 99, 106892.
De, S. K., & Mahata, G. C. (2021). Solution of an imperfect-quality EOQ model with backorder under fuzzy lock leadership game approach. International Journal of Intelligent Systems, 36(1), 421-446.
Harris, F. W. (1913) ‘Operations and costs (Factory Management Series)’, A.W. Shaw Co, Chicago, pp.18-52.
Jaggi, C. K. (2014). An optimal replenishment policy for non-instantaneous deteriorating items with price-dependent demand and time-varying holding cost. International Scientific Journal on Science Engineering & Technology, 17(03).
Jaggi, C., Sharma, A., & Tiwari, S. (2015). Credit financing in economic ordering policies for non-instantaneous deteriorating items with price dependent demand under permissible delay in payments: A new approach. International Journal of Industrial Engineering
Computations, 6(4), 481-502.
Karlin, S. (1958). One stage inventory models with uncertainty. Studies in the mathematical theory of inventory and production, 109-134.
Khanna, A., Gautam, P., & Jaggi, C. K. (2017). Inventory modeling for deteriorating imperfect quality items with selling price-dependent demand and shortage
back-ordering under credit financing. International Journal of Mathematical, Engineering and Management Sciences, 2(2), 110-124.
Karmakar, S., De, S. K., & Goswami, A. (2018). A pollution-sensitive remanufacturing model with waste items: triangular dense fuzzy lock set approach. Journal of Cleaner Production, 187, 789-803.
Maddah, B., & Jaber, M. Y. (2008). The economic order quantity for items with imperfect quality: revisited. International Journal of Production Economics, 112(2), 808-815.
Mohagheghian, E., Rasti-Barzoki, M., & Sahraeian, R. (2018). Two-Echelon Supply Chain Considering Multiple Retailers with Price and Promotional Effort Sensitive Non-Linear Demand. Journal of Optimization in Industrial Engineering, 11(2), 57-64.
Mao, X. B., Hu, S. S., Dong, J. Y., Wan, S. P., & Xu, G. L. (2018). Multi-attribute group decision-making based on cloud aggregation operators under interval-valued hesitant fuzzy linguistic environment. International Journal of Fuzzy Systems, 20(7), 2273- 2300.
Mohamadghasemi, A. (2020). A Note On “An Interval Type-2 Fuzzy Extension Of The TOPSIS Method Using Alpha Cuts”. Journal of Optimization in Industrial Engineering, 13(2), 227- 238.
Maity, S., Chakraborty, A., De, S. K., Mondal, S. P., & Alam, S. (2020) A comprehensive study of a backlogging EOQ model with the nonlinear heptagonal dense fuzzy environment. RAIRO-Operations Research, 54(1), 267-286.
Peng, X., & Yang, Y. (2015). Some results for Pythagorean fuzzy sets. International Journal of Intelligent Systems, 30(11), 1133-1160.
Peng, X., & Selvachandran, G. (2019). Pythagorean fuzzy set: state of the art and future directions. Artificial Intelligence Review, 52(3), 1873-1927.
Peng, X. (2019). New operations for interval-valued Pythagorean fuzzy set. Scientia Iranica.Transaction E, Industrial Engineering, 26(2), 1049-1076.
Patro, R., Nayak, M. M., & Acharya, M. (2019). An EOQ model for fuzzy defective rate with allowable proportionate discount. O psearch, 56(1), 191-215.
Pal, S., & Chakraborty, A. (2020). Triangular neutrosophic-based EOQ model for non-instantaneous deteriorating item under shortages. Am J Bus Oper Res, 1(1), 28-35.
Pattnaik, S, & Nayak, M. (2021). Linearly Deteriorating EOQ Model for Imperfect Items with Price Dependent Demand Under Different Fuzzy Environments. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 5328-5349.
Rosenblatt, M. J., & Lee, H. L. (1986). Economic production cycles with imperfect production processes. IIE transactions, 18(1), 48-55.
Salameh, M. K., & Jaber, M. Y. (2000). Economic production quantity model for items with imperfect quality. International journal of production economics, 64(1-3), 59-64.
Singh, S. R., & Singh, C. (2008). Fuzzy inventory model for finite rate of replenishment using signed distance method. International Transactions in Mathematical Sciences and Computer, 1(1), 21-30.
Shekarian, E., Olugu, E. U., Abdul-Rashid, S. H., & Kazemi, N. (2016). An economic order quantity model considering different holding costs for imperfect quality items subject to fuzziness and learning. Journal of Intelligent & Fuzzy Systems, 30(5), 2985-2997.
Shekarian, E., Kazemi, N., Abdul-Rashid, S. H., & Olugu, E. U. (2017). Fuzzy inventory models: A comprehensive review. Applied Soft Computing, 55, 588-621.
Wee, H. M., Yu, J., & Chen, M. C. (2007). Optimal inventory model for items with imperfect quality and shortage backordering. Omega, 35(1), 7-11.
Wahab, M. I. M., & Jaber, M. Y. (2010). Economic order quantity model for items with imperfect quality, different holding costs, and learning effects: A note. Computers & Industrial Engineering, 58(1), 186-190.
Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143-161.
Zadeh, L.A. (1965).Fuzzy set information and control. 8(3), 338- 353.
