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Optimal Replenishment Policy for Ameliorating Item with Shortages under Inflation and Time Value of Money using Genetic Algorithm

by S.R. Singh, Tarun Kumar, C. B. Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 1
Year of Publication: 2011
Authors: S.R. Singh, Tarun Kumar, C. B. Gupta
10.5120/3269-4431

S.R. Singh, Tarun Kumar, C. B. Gupta . Optimal Replenishment Policy for Ameliorating Item with Shortages under Inflation and Time Value of Money using Genetic Algorithm. International Journal of Computer Applications. 27, 1 ( August 2011), 5-17. DOI=10.5120/3269-4431

@article{ 10.5120/3269-4431,
author = { S.R. Singh, Tarun Kumar, C. B. Gupta },
title = { Optimal Replenishment Policy for Ameliorating Item with Shortages under Inflation and Time Value of Money using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 1 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number1/3269-4431/ },
doi = { 10.5120/3269-4431 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:39.176784+05:30
%A S.R. Singh
%A Tarun Kumar
%A C. B. Gupta
%T Optimal Replenishment Policy for Ameliorating Item with Shortages under Inflation and Time Value of Money using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 1
%P 5-17
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The implementation of supply chain has to reduce the total cost of system, but generally each component of a supply chain tries to find the best policy for its company and consequently tries to find a local optimum. Knowing that the sum of local optimum cannot constitute the global optimum, it is necessary to consider all costs of system simultaneously to find the optimal replenishment policy for all the components of a supply chain. Demand rate of the item is assumed to be a function of time known as ramp type function. Shortages are permitted and partially back-ordered. The back-ordering fraction is taken to be decreasing function of waiting time. We consider inflation and apply discounted cash flow in the problem analysis. Total cost of the system is formulated and optimal replenishment policy is derived, keeping in view the above factors of the system. We use Genetic Algorithm (GA) to solve the models. A numerical example and sensitivity analysis is shown to illustrate the models.

References
  1. Skouri, K. and Papachristos, S. (2003), “Optimal stopping and restarting production times for an EOQ model with deteriorating items and time-dependent partial backlogging”, International Journal of Production Economics, Vol.81-82, pp.525-531.
  2. Panda, S., Senapati, S. and Basu, M. (2008). “Optimal replenishment policy for perishable seasonal products in a season with ramp-type time dependent demand”. Computers & Industrial Engineering 54, 301–314.
  3. Benkherouf, L. and Balkhi, Zaid T. (1997). “On an Inventory Model for Deteriorating Items and Time-Varying Demand.” Mathematical Methods of Operations Research,45,221-233.
  4. Chakrabarty, T., Giri, B.C. and Chauduri, K.S. (1997). “An EOQ model for items with Weibull distribution deterioration, shortages and trended demand: An Extension of Philip’s model.” Computers Ops. Res. ,25, 7/8,649-657.
  5. Sıla, C., M. Fatih and L. Chung-Yee (2005). "A comparison of out bound dispatch policies for integrated inventory and transportation decisions." European Journal of Operational Research.
  6. Jen-Ming, C. and C. Tsung-Hui (2005). "The multi-item replenishment problem in a two-echelon supply chain: the effect of centralization versus decentralization." Computers & Operations Research 32.
  7. Bahloul, K., A. Baboli and J.-P. Campagne (2008). “Optimization methods for inventory and transportation problem in Supply Chain: literature review.” International Conference on Information Systems, Logistics and Supply Chain (ILS). Madison, WI , U.S.A.
  8. Datta, T.K., and Pal, A.K. (1991). “Effects of inflation and Time-value of money on an inventory model with linear time-dependent demand rate and shortages.” European Journal of Operational Research ,52,326-333.
  9. Datta, T.P., and Pal, A.K. (1988). “Order level inventory system with power demand patterns for items with variable rate of deterioration.” Indian J of pure App. Math.,19(11);1043-1053.
  10. Ghare, P. M., & Schrader, G. F. (1963). “A model for an exponentially decaying inventory.” Journal of Industrial Engineering, 14, 238–243.
  11. Goyal S.K. (1987). “Economic ordering policies for deteriorating items over an infinite time horizon.” European Journal of Operational Research, 28, 298-301.
  12. Hariga, M. (1995). “Effects of inflation and time-value of money on an inventory model with time-dependent demand rate and shortages.” European Journal of Operational Research,81,512-520.
  13. Hariga, M. (1997). “Optimal inventory policies for perishable items with time-dependent demand.” Int. J. Production Economics,50,35-41.
  14. Hill, R.M. (1995). “Inventory model for increasing demand followed by level demand.” Journal of the Operational Research Society 46, 1250–1259.
  15. Silver E. A. and Meal, H. C., 1969. A simple modification of the EOQ for the case of a varying demand rate. Production of Inventory Management.10 (4), 52-65.
  16. Hwang, H. S. (1997). “A study on an inventory model for items with Weibull ameliorating.” Computers and Industrial Engineering, 33, 701–704.
  17. Hwang, H. S. (1999). “Inventory models for both deteriorating and ameliorating items.” Computers and Industrial Engineering, 37, 257–260.
  18. Lin, Y. and Lin, C. (2006). “Purchasing model for deteriorating items with time-varying demand under inflation and time discounting.” ,Int J Adv Manuf. Technology 27: 816–823.
  19. Mondal, B., & Pal, A. K. (1998). “Order level inventory system with ramp type demand for deteriorating items.” Journal of Interdisciplinary Mathematics, 1, 49–66.
  20. Moon, I., Giri, B.C., Ko, B.(2005). “Economic order quantity models for ameliorating/deteriorating items under inflation and time discounting.” European Journal of Operational Research 162, 773–785.
  21. Chih-Yao Lo, (2008). “Advance of Dynamic Production-Inventory Strategy for Multiple Policies using Genetic Algorithm”, Information Technology Journal, Vol. 7, pp. 647-653..
  22. Skouri, K. , Konstantaras, I., Papachristos, S., Ganas, I.(2009). “Inventory models with ramp type demand rate, partial backlogging and Weibull deterioration rate.” European Journal of Operational Research, 192, 79–92.
  23. Wu, K.S. & Ouyang, L.Y. (2000). “A Replenishment Policy for Deteriorating Items with Ramp Type Demand Rate.” Proc. Natl. Sci. Counc. ROC(A) ,24(4), 279-286.
  24. Wee, H. M.(1999). “Deteriorating inventory model with quantity discount, pricing and partial backordering.” International Journal of Production Economics, 59, 511-518.
  25. Wu, K., S.(2001). “An EOQ inventory model for items with Weibull distribution deterioration, ramp type demand rate and partial backlogging.” Production Planning and Control, 12 (8), 787–793.
  26. Zhou, Y.W., Lau, H.S. and Yang S.L. (2004). “A finite horizon lot-sizing problem with time-varying deterministic demand and waiting-time-dependent partial backlogging.” Int. J. Production Economics,91,109–119.
  27. Huang, Guobo, (1995). Modelling China's demand for international reserves. Applied Financial Economics, 5, pp. 357 – 366.
  28. Yang, P.C. (2004). “Pricing strategy for deteriorating items using quantity discount when demand is price sensitive”. European Journal of Operational Research, 157(2), 389-397.
  29. L. Davis, (1991). “The Handbook of Genetic Algorithms” , Van Nostrand Reinhold, New York .
  30. D.E. Goldberg,(1989). “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley, Reading, MA .
  31. J.H. Holl and,(1975).” Adaptation in Natural and Artificial Systems”, The University of Michigan Press, Ann Arbor, IL .
  32. Z. Michalewicz,1994,” Genetic Algorithms + Data Structures = Evolution Programs”. AI Series, Springer, New York .
Index Terms

Computer Science
Information Sciences

Keywords

Replenishment policy ramp type demand amelioration items Genetic algorithm

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