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EOQ Model with Partial Backordering for Imperfect Items under the Effect of Inflation and Learning with Selling Price Dependent Demand

by Dharmendra Yadav, S.R. Singh, Meenu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 17
Year of Publication: 2015
Authors: Dharmendra Yadav, S.R. Singh, Meenu
10.5120/19759-1561

Dharmendra Yadav, S.R. Singh, Meenu . EOQ Model with Partial Backordering for Imperfect Items under the Effect of Inflation and Learning with Selling Price Dependent Demand. International Journal of Computer Applications. 111, 17 ( February 2015), 25-31. DOI=10.5120/19759-1561

@article{ 10.5120/19759-1561,
author = { Dharmendra Yadav, S.R. Singh, Meenu },
title = { EOQ Model with Partial Backordering for Imperfect Items under the Effect of Inflation and Learning with Selling Price Dependent Demand },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 17 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number17/19759-1561/ },
doi = { 10.5120/19759-1561 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:48:10.483305+05:30
%A Dharmendra Yadav
%A S.R. Singh
%A Meenu
%T EOQ Model with Partial Backordering for Imperfect Items under the Effect of Inflation and Learning with Selling Price Dependent Demand
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 17
%P 25-31
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the most fruitful areas in the line of inventory is that the deficiency of handling/ production facilities can be overcome through a natural phenomenon known as learning effect. Due to this the performance of service and manufacturing organizations engaged in a repetitive process improves with time. The proposed economic order quantity model (EOQ) in this paper has been made realistic by analyzing the impact of learning. All of the study is carried out in inflationary environment. It is very obvious fact that given some time, every item can create a niche for itself in the customer’s mind, hence increasing its demand with the passage of time. The selling price of a product is one of the crucial factors in selecting the item for use. Keeping this in mind, an inventory model is developed by taking selling price dependent demand. In this model, it is assumed that the received items are not of perfect quality and after100% screening, imperfect items are withdrawn from inventory and sold at discounted price. Finally, the feasibility and applicability of model are shown through numerical analysis. Sensitivity analysis is also performed with respect to different inventory parameters.

References
  1. Burwell T. H., Dave D. S., Fitzpatrick K. E. and Roy M. R. (1997). Economic lot size model for price-dependent demand under quantity and freight discounts, International Journal of Production Economics, Vol. 48(2), pp.141-155.
  2. Buzacott, J.A. (1975). Economic order quantities with inflation. Operational Research Quarterly, Vol. 26, pp. 553-558.
  3. Chang, C.T., Chen, Y.J., Tsai, T.R., and Wu, S.J. (2010). Inventory models with stock-and price dependent demand for deteriorating items based on limited shelf space. Yugoslav Journal of Operations Research, Vol. 20(1), pp.55-69.
  4. Chang, H.C. (2004). An application of fuzzy sets theory to the EOQ model with imperfect quality items. Computers and Operational Research, Vol. 31, pp. 2079-2092.
  5. Chern, M.S., Yang, H.L., Teng, J.T., and Papachristos, S. (2008). Partial backlogging inventory lot-size models for deteriorating items with fluctuating demand under inflation. European Journal of Operational Research, Vol.191 (1), pp.127-141.
  6. Goyal, S.K., and Cardenas-Barron, L.E. (2002). Note on economic production quality model for items with imperfect quality-A practical Approach. International Journal of Production Economics, Vol. 77, pp. 85-87.
  7. Goyal, S.K., Singh, S.R., and Yadav, D. (2015). A New Approach for EOQ Model for Imperfect Lot Size with Mixture of Backorder and Lost Sale under Advertisement Dependent Imprecise Demand. International Journal of Operational Research, (accepted).
  8. Huang, C.K. (2004). An optimal policy for a single-vendor single-buyer integrated production-inventory problem with process unreliability. International Journal of Production Economics, Vol.91, pp. 91-98.
  9. Jaber, M.Y., Goyal, S.K., and Imran, M. (2008). Economic production quantity model for items with imperfect quality subject to learning effects. International Journal of Production Economics, Vol.115, pp.143-150.
  10. Kim, C.H., and Hong, Y. (1999). An optimal production run length in deteriorating production process. International Journal of Production Economics, Vol. 58, pp. 183-189.
  11. Kim, D.H., and Park, K.S. (1985). (Q, r) inventory model with a mixture of sales and time weighted backorders. Journal of the Operational Research Society, Vol.36, pp.231-238.
  12. Liao, H.C., Tsai, C.H., and Su, C.T. (2000). An inventory model with deteriorating items under inflation when a delay in payment is permissible. International Journal of Production Economics, Vol.63, pp. 207-214.
  13. Maddah, B., and Jaber, M.Y. (2008). Economic order quantity for items imperfect quality: Revisited. International Journal of Production Economics, Vol.112, pp.808-815.
  14. Maiti, A., Mait, M., and Maiti, M. (2009). Inventory model with stochastic lead-time and price dependent demand in corporating advance payment. Applied Mathematical Modelling, Vol. 33(5), pp. 2433-2443.
  15. Min, J., Zhou, Y.W., Liu, G.Q., and Wang, S.D. (2012). An EPQ Model for Deteriorating Items with Inventory-Level-Dependent Demand and Permissible Delay in Payments. International Journal of Systems Science, Vol. 43(6), pp.1039-1053.
  16. Misra, R.B. (1975). A study of inflationary effects on inventory systems. Logistic Spectrum, Vol. 9(3), pp.260-268.
  17. Mondal B., Bhunia A. K. and Maiti M. (2003). An inventory system of ameliorating items for price dependent demand rate, Computers and Industrial Engineering, Vol. 45(3), pp. 443-456.
  18. Pan, C.H., and Hsiao, Y.C. (2005). Integrated inventory models with controllable lead time and backorder discount considerations. International Journal of Production Economics, Vol.93, pp.387-397.
  19. Papachristos, S., and Konstantaras, I. (2006). Economic ordering quantity models for items with imperfect quality. International Journal of Production Economics, Vol.100, pp.148-154.
  20. Rosenblatt, M.J., and Lee, H.L. (1986). Economic production cycles with imperfect production process. IEE Transactions, Vol. 18, pp. 48-55.
  21. Roy. M.D., Sana, S.S., and Chaudhuri, K.S. (2011). An economic order quantity model of imperfect quality items with partial backlogging. International Journal of Systems Science, Vol. 42(8), pp. 1409-1419.
  22. Salameh, M.K., and Jaber, M.Y. (2000). Economic production quantity model for item of imperfect quality. International Journal of Production Economics, Vol. 64, pp.59-64.
  23. Shastri, A., Singh, S.R., Yadav, D., and Gupta, S. (2014). Supply chain management for two-level trade credit financing with selling price dependent demand under the effect of preservation technology. Int. J. of Procurement Management, Vol. 7(6), pp.695-718.
  24. Teng, J. T., Chang, C. T., and Goyal, S. K. (2005). Optimal pricing and ordering policy under permissible delay in payments, International Journal of Production Economics, Vol. 97, pp.121-129.
  25. Whitin, T. M. (1955). Inventory control and price theory. Management Sci., Vol. 2, pp. 61–80.
  26. Yadav, D., Singh, S.R., and Kumari, R. (2012). Effect of Demand Boosting Policy on Optimal Inventory Policy for Imperfect Lot Size and with Backorder in Fuzzy Environment. Control and Cybernetics, Vol. 43(1), pp.191-212.
  27. Yadav, D., Singh, S.R., and Kumari, R. (2012). Effects of Learning on Optimal Lot Size and Profit in Fuzzy Environment. International Journal of Operational and Quantitative Management, Vol. 18(2), pp.145-158.
  28. Yadav, D., Singh, S.R., and Kumari, R. (2015). Retailer’s optimal policy under inflation in fuzzy environment with trade credit. International Journal of Systems Science, Vol. 46(4), pp. 754-762.
  29. You S. P. (2005). Inventory policy for products with price and time-dependent demands. Journal of Operational Research Society, Vol.56, pp.870-873.
Index Terms

Computer Science
Information Sciences

Keywords

FFT Sentiment Analysis Natural Language Processing Fuzzy logic.

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