A reverse logistic inventory model for imperfect production process with preservation technology investment under learning and inflationary environment

Jawla, P.; Singh, S.

Growing Science » Uncertain Supply Chain Management » A reverse logistic inventory model for imperfect production process with preservation technology investment under learning and inflationary environment

Journals

USCM Volumes

Volume 1 (22)
- Issue 1 (4)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (6)

Volume 2 (32)
- Issue 1 (7)
- Issue 2 (5)
- Issue 3 (10)
- Issue 4 (10)

Volume 3 (39)
- Issue 1 (9)
- Issue 2 (13)
- Issue 3 (10)
- Issue 4 (7)

Volume 4 (31)
- Issue 1 (10)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (9)

Volume 5 (26)
- Issue 1 (6)
- Issue 2 (6)
- Issue 3 (8)
- Issue 4 (6)

Volume 6 (25)
- Issue 1 (7)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (6)

Volume 7 (57)
- Issue 1 (8)
- Issue 2 (19)
- Issue 3 (14)
- Issue 4 (16)

Volume 8 (82)
- Issue 1 (20)
- Issue 2 (15)
- Issue 3 (17)
- Issue 4 (30)

Volume 9 (117)
- Issue 1 (25)
- Issue 2 (26)
- Issue 3 (32)
- Issue 4 (34)

Volume 10 (150)
- Issue 1 (28)
- Issue 2 (32)
- Issue 3 (44)
- Issue 4 (46)

Volume 11 (190)
- Issue 1 (42)
- Issue 2 (45)
- Issue 3 (50)
- Issue 4 (53)

Volume 12 (244)
- Issue 1 (55)
- Issue 2 (59)
- Issue 3 (63)
- Issue 4 (67)

Volume 13 (30)
- Issue 1 (15)
- Issue 2 (15)

Keywords

Authors

Countries

Uncertain Supply Chain Management

ISSN 2291-6830 (Online) - ISSN 2291-6822 (Print) Quarterly Publication Volume 4 Issue 2 pp. 107-122 , 2016

A reverse logistic inventory model for imperfect production process with preservation technology investment under learning and inflationary environment Pages 107-122 Download PDF

Authors: Preeti Jawla, S. R. Singh

DOI: 10.5267/j.uscm.2015.12.001

Keywords: Exponential demand rate, Exponential holding cost, Imperfect production, Inflation, Learning, Multi-items, Preservation, Reverse logistics

Abstract: This paper presents a unified multi items general inventory model for integrated production of new items and remanufacturing of returned and defected items for a finite planning horizon. In this paper, a production model that takes into account learning, instantaneous deterioration rate and inflation is proposed. In addition, we also consider that the holding cost is a non-negative, non-decreasing and continuous function of time. In this model, the preservation technology is used to reduce the rate of product deterioration. A theory is developed to find the optimal solution of the proposed model; it is then exemplified with the help of several numerical examples. An efficient solution procedure is also provided to find the optimal strategy. Finally, sensitivity of the optimal solution to changes in the values of different parameters of the system and the convexities of the cost functions are also studied and represented through the graphs.

How to cite this paper

 Jawla, P & Singh, S. (2016). A reverse logistic inventory model for imperfect production process with preservation technology investment under learning and inflationary environment.Uncertain Supply Chain Management, 4(2), 107-122.

Refrences

Badiru, A.B. (1995). Multivariate analysis of the effect of learning and forgetting on product quality. International Journal of Production Research, 33(3), 777–794.

Buzacott, J.A. (1975). Economic order quantities with inflation. Operational Research Quarterly, 26(3), 1188-1191.

Covert, R.P., & Philip, G.C. (1973). An EOQ model with Weibull distribution deterioration. AIIE Transactions, 5(4), 323–326.

Dekker, R., Fleischmann, M., Inderfurth, K., & van Wassenhove, L. N. (Eds.). (2013). Reverse logistics: quantitative models for closed-loop supply chains. Springer Science & Business Media.

Dobos, I., & Richter, K. (2003). A production/recycling model with stationary demand and return rates. Central European Journal of Operations Research, 11(1), 35–46.

Dye, CY., & Hsieh, TP. (2012). An optimal replenishment policy for deteriorating items with effective investment in preservation technology. European Journal of Operational Research, 218(1), 106–112.

Garvin, D.A. (1988). Managing Quality. Free Press, New York.

Ghare, P.M., & Schrader, G.H. (1963). A model for an exponentially decaying inventory. Journal of Industrial Engineering, 14(5), 238–243.

Guide, V. D. R., Jayaraman, V., & Linton, J. D. (2003). Building contingency planning for closed-loop supply chains with product recovery. Journal of operations Management, 21(3), 259-279.

Gupta, R., & Vrat, P. (1986). Inventory model with multi-items under constraint systems for stock dependent consumption rate. Operations Research, 24(1), 41-42.

Jaber, M. Y., & El Saadany, A. M. (2011). An economic production and remanufacturing model with learning effects. International Journal of Production Economics, 131(1), 115-127.

Jaber, M.Y., & Bonney, M. (2003). Lot sizing with learning and forgetting in set-ups and in product quality. International Journal of Production Economics, 83(1), 95–111.

Johnson, M.R., & Wang, M.H. (1998). Economical evaluation of disassembly operations for recycling, remanufacturing and reuse. International Journal of Production Research, 36(12), 3227–3252.

Krumwiede, D. W., & Sheu, C. (2002). A model for reverse logistics entry by third-party providers. Omega, 30(5), 325-333.

Liao, H. C., Tsai, C. H., & Su, C. T. (2000). An inventory model with deteriorating items under inflation when a delay in payment is permissible.International Journal of Production Economics, 63(2), 207-214.

Nahmias, S., & Rivera, H. (1979). A deterministic model for a repairable item inventory system with a finite repair rate. International Journal of Production Research, 17(3), 215–221.

Ouyang, L. Y., Wu, K. S., & Yang, C. T. (2006). A study on an inventory model for non-instantaneous deteriorating items with permissible delay in payments. Computers & Industrial Engineering, 51(4), 637-651.

Ray, J., & Chaudhuri, K. S. (1997). An EOQ model with stock-dependent demand, shortage, inflation and time discounting. International Journal of Production Economics, 53(2), 171-180.

Sarkar, B., & Moon, I. (2011). An EPQ model with inflation in an imperfect production system. Applied Mathematics and Computation, 217(13), 6159-6167.

Schrady, D. A. (1967). A deterministic inventory model for reparable items.Naval Research Logistics Quarterly, 14(3), 391-398.

Singh, S. R., Jain, S., & Pareek, S. (2012). A warehouse imperfect fuzzified production model with shortages under inflationary conditions. Advances in Decision Sciences, 2012, Article ID638060, 16 pages.

Singh, S., Jain, S., & Pareek, S. (2013). An imperfect quality items with learning and inflation under two limited storage capacity. International Journal of Industrial Engineering Computations, 4(4), 479-490.

Singh, S., Prasher, L., & Saxena, N. (2013). A centralized reverse channel structure with flexible manufacturing under the stock out situation.International Journal of Industrial Engineering Computations, 4(4), 559-570.

Singh, SR., & Saxena, N. (2012). An Optimal Returned Policy for a Reverse Logistics Inventory Model with Backorders. Advances in Decision Sciences, Article ID 386598: 21 pages.

Wee, H., Hsu, P., & Teng, H. (2010). Preservation technology investment for deteriorating inventory. International Journal of Production Economics, 124(2), 388–394.

Wright, T. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Science, 3(2), 122–128.

Wu, K.S., Ouyang, L.Y., & Yang, C.T. (2006). An optimal replenishment policy for non- instantaneous deteriorating items with stock-dependent demand and partial backlogging. International Journal of Production Economics, 101(2), 369–384.

Yang, P. C., Chung, S. L., Wee, H. M., Zahara, E., & Peng, C. Y. (2013). Collaboration for a closed-loop deteriorating inventory supply chain with multi-retailer and price-sensitive demand. International Journal of Production Economics, 143(2), 557-566.

Yelle, L.E. (1979). The learning curve: Historical review and comprehensive survey. Decision Sciences, 10(2), 302–328.