Abstract
The newsvendor model is very simple, but it is very important to study problems of supply chain coordination. This model is also called a single-cycle inventory model. In a short cycle, it is very difficult to forecast the demand. The effect of good prediction reduces inventory shortage, increases the profit and enhances competitiveness. This type of product is characterized by strong uncertainty, that means demand always changes. This creates challenges for decision makers. For newly developed products, it is very difficult to obtain the statistical demand distribution of a product. Here, we consider the demand of the milk products, which are fluctuating in nature. Hence, demand is a random variable. It is assumed that demand follows Weibull distribution. Also, there is some uncertainty in demand. It leads to the over estimation or under estimation of the parameters of the distribution. Fuzzy triangular numbers are used to obtain the uncertain demand. The lifetime of the items are very short. Thus, unsold items are sent back to the manufacturers. The retailers and manufacturers profit is obtained under decentralized and centralized supply chain. Also,the proposed single period (newsboy) inventory model is used to obtain an optimal order quantity. The proposed methodology is illustrated for milk product from Aundh market Pune, India.
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References
Chang H (2004) An application of fuzzy set theory to the EOQ model with imperfect quality items. Comput Oper Res 31(12):2079–2092
Chen SM, Wang JY (1995) Document retrieval using knowledge-based fuzzy information retrieval techniques. IEEE Trans Syst Man Cybern 25(5):793–803
Chen SM, Chang YC (2011) Weighted fuzzy rule interpolation based on GA-based weight-learning techniques. IEEE Trans Fuzzy Syst 19(4):729–744
Chen SP, Ho YH (2011) Analysis of the newsboy problem with fuzzy demands and incremental discounts. Int J Prod Econ 129:169–177
Chen SM, Tanuwijaya K (2011) Fuzzy forecasting based on high-order fuzzy relationship and automatic clustering techniques. Expert Syst Appl 38(12):15425–15437
Chen SM, Chu HP, Sheu TW (2012) TAIEX forecasting using fuzzy time series and automatically generated weights of multiple factors. IEEE Trans Syst Man Cybern 42(6):1485–1495
Chen SM, Manalu GMT, Pan JS, Liu HC (2013) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Trans Cybern 43(3):1102–1117
Chen SM, Shen WC (2015) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships. IEEE Trans Cybern 45(3):405–417
Cheng SH, Chen SM, Jian WS (2016) Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf Sci 327:272–287
Dutta P, Chakraborty D (2005) A single period inventory model with fuzzy random variable demand. Math Comput Model 41:915–922
Dutta P, Chakraborty D (2010) Incorporating one way substitution policy into the newsboy problem with imprecise customer demand. Eur J Oper Res 200:99–110
Erlenkotter D (1989) Note an early classic misplaced: Ford W. Harris’s Economic order quantity model of 1915. Manag Sci 35(7):898–900
Hu J, Zheng H, Xu R, Ji Y, Guo C (2010) Supply chain coordination for fuzzy random newsboy problem with imperfect quality. Int J Approx Reason 51:771–784
Kwakernaak H (1978) Fuzzy random variables-I. Definitions and theorems. Inf Sci 15(1):1–29
Latpate RV, Bajaj VH (2011a) Fuzzy programming for multi-objective transportation and inventory management problem with retailer storage. Int J Agric Stat Sci 7(1):317–326
Latpate RV, Bajaj VH (2011b) Fuzzy multi-objective, multi-product, production distribution problem with manufacturer storage. Proc of Int Congress on PQROM, New Delhi, pp 340–355
Latpate RV (2014) Fuzzy multi-objective supplier selection problem for multiple items in a supply chain. Int J Innov Res Comput Sci Technol 2(5):2347–5552
Latpate RV, Kurade SS (2017) Fuzzy MOGA for supply chain model with pareto decision space at different alpha-cuts. Int J Adv Manuf Technol 91:3861–3876. https://doi.org/10.1007/s00170-016-9966-5
Petrovic D, Petrovic R (1999) Supply chain modeling using fuzzy sets. Int J Prod Econ 59:443–445
Petrovic D, Petrovic R, Vujosevic M (1996) Fuzzy models for newsboy problem. Int J Prod Econ 45:435–441
Puri ML, Ralescu D (1986) Fuzzy random variables. J Math Anal Appl 114:409–422
Rudin W (1976) Principles of mathematical analysis, 3rd edn. McGraw-Hill Inc, New York
Totan G, Dipankar C, Tapan KR (2019) A fuzzy rough multi-objective multi-item inventory model with both stock- dependent demand and holding cost rate. Granul Comput 4:71–88
Wang XB, Wang SH (2009) Supply chain coordination based on buyback contract with fuzzy demand. IEEE Proc Int Conf Mach Learn Cybern Heibei China 2:839–843
Xu R, Zhai X (2008) Optimal models for single period supply chain problem with fuzzy demand. Inf Sci 178:3374–3381
Zadeh LA (1965) Fuzzy Sets. Inf Control 8:338–353
Zhang B, Lu S, Zhang D, Wen K (2014) Supply chain coordination based on a buyback contract under fuzzy random variable demand. Fuzzy Sets Syst 255:1–16
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Bhosale, M.R., Latpate, R.V. Single stage fuzzy supply chain model with Weibull distributed demand for milk commodities. Granul. Comput. 6, 255–266 (2021). https://doi.org/10.1007/s41066-019-00186-2
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DOI: https://doi.org/10.1007/s41066-019-00186-2