全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

A Partial Backlogging Inventory Model for Deteriorating Item under Fuzzy Inflation and Discounting over Random Planning Horizon: A Fuzzy Genetic Algorithm Approach

DOI: 10.1155/2013/973125

Full-Text   Cite this paper   Add to My Lib

Abstract:

An inventory model for deteriorating item is considered in a random planning horizon under inflation and time value money. The model is described in two different environments: random and fuzzy random. The proposed model allows stock-dependent consumption rate and shortages with partial backlogging. In the fuzzy stochastic model, possibility chance constraints are used for defuzzification of imprecise expected total profit. Finally, genetic algorithm (GA) and fuzzy simulation-based genetic algorithm (FSGA) are used to make decisions for the above inventory models. The models are illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented. Scope and Purpose. The traditional inventory model considers the ideal case in which depletion of inventory is caused by a constant demand rate. However, to keep sales higher, the inventory level would need to remain high. Of course, this would also result in higher holding or procurement cost. Also, in many real situations, during a longer-shortage period some of the customers may refuse the management. For instance, for fashionable commodities and high-tech products with short product life cycle, the willingness for a customer to wait for backlogging is diminishing with the length of the waiting time. Most of the classical inventory models did not take into account the effects of inflation and time value of money. But in the past, the economic situation of most of the countries has changed to such an extent due to large-scale inflation and consequent sharp decline in the purchasing power of money. So, it has not been possible to ignore the effects of inflation and time value of money any more. The purpose of this paper is to maximize the expected profit in the random planning horizon. 1. Introduction In the past few decades, many researches have studied an inventory model with constant demand or dynamic demand (cf. M. K. Maiti and M. Maiti [1], Taleizadeh et al. [2], Jana et al. [3], and others). Moreover, in a competitive situation attractive display of units in the showroom is an important factor. Levin et al. [4] noted that at times the presence of inventory has a motivational effect on the people around it. It is a common belief that large piles of goods displayed in a supermarket will lead the customer to buy more. Thus, many business people use showrooms and the attractive display of units in the showroom to influence the customers. Roy et al. [5] and Maiti [6] have developed an inventory model with stock-dependent demand. In most of the earlier inventory models,

References

[1]  M. K. Maiti and M. Maiti, “Fuzzy inventory model with two warehouses under possibility constraints,” Fuzzy Sets and Systems, vol. 157, no. 1, pp. 52–73, 2006.
[2]  A. A. Taleizadeh, S. T. A. Niaki, and R. Nikousokhan, “Constraint multiproduct joint-replenishment inventory control problem using uncertain programming,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 5143–5154, 2011.
[3]  D. K. Jana, K. Maity, and T. K. Roy, “A bi-fuzzy approach to a production-recycling-disposal inventory problem with environment pollution cost via genetic algorithm,” International Journal of Computer Applications, vol. 61, pp. 1–10, 2013.
[4]  R. I. Levin, C. P. Mcaughlim, P. R. Lamone, and J. F. Kottas, Production Management/ Operations Management: (Contemporary Policy for Managing Operating System), McGraw-Hill, New York, NY, USA, 1972.
[5]  A. Roy, M. K. Maiti, S. Kar, and M. Maiti, “Two storage inventory model with fuzzy deterioration over a random planning horizon,” Mathematical and Computer Modelling, vol. 46, no. 11-12, pp. 1419–1433, 2007.
[6]  M. K. Maiti, “A fuzzy genetic algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon,” European Journal of Operational Research, vol. 213, no. 1, pp. 96–106, 2011.
[7]  J. A. Buzacott, “Economic order quantities with inflation,” Operational Research Quarterly, vol. 26, no. 3, pp. 553–558, 1975.
[8]  R. B. Misra, “A study of inflationary effects on inventory systems,” Logistic Spectrum, vol. 9, pp. 260–268, 1975.
[9]  H. Bierman and J. Thomas, “Inventory decisions under inflationary conditions,” Decision Sciences, vol. 8, pp. 151–155, 1997.
[10]  R. B. Misra, “Note on optimal inventory management under inflation,” Naval Research Logistics Quarterly, vol. 26, no. 1, pp. 161–165, 1979.
[11]  M.-S. Chern, H.-L. Yang, J.-T. Teng, and S. Papachristos, “Partial backlogging inventory lot-size models for deteriorating items with fluctuating demand under inflation,” European Journal of Operational Research, vol. 191, no. 1, pp. 127–141, 2008.
[12]  K. Maity and M. Maiti, “A numerical approach to a multi-objective optimal inventory control problem for deteriorating multi-items under fuzzy inflation and discounting,” Computers & Mathematics with Applications, vol. 55, no. 8, pp. 1794–1807, 2008.
[13]  H. L. Yang, J. T. Teng, and M. S. Chern, “An inventory model under inflation for deteriorating items with stock-dependent consumption rate and partial backlogging shortages,” International Journal of Production Economics, vol. 123, pp. 8–19, 2010.
[14]  Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin, Germany, 1992.
[15]  M. Bessaou and P. Siarry, “A genetic algorithm with real-value coding to optimize multimodal continuous functions,” Structural and Multidisciplinary Optimization, vol. 23, no. 1, pp. 63–74, 2001.
[16]  M. Last and S. Eyal, “A fuzzy-based lifetime extension of genetic algorithms,” Fuzzy Sets and Systems, vol. 149, no. 1, pp. 131–147, 2005.
[17]  F. Pezzella, G. Morganti, and G. Ciaschetti, “A genetic algorithm for the flexible job-shop scheduling problem,” Computers and Operations Research, vol. 35, no. 10, pp. 3202–3212, 2008.
[18]  D. Dubois and H. Prade, Fuzzy Sets and Systems, Theory and Applications, vol. 144, Academic Press, New York, NY, USA, 1980.
[19]  R. Narmatha Banu and D. Devaraj, “Multi-objective GA with fuzzy decision making for security enhancement in power system,” Applied Soft Computing, vol. 12, pp. 2756–2764, 2012.
[20]  S. Kar, D. Das, and A. Roy, “A production-inventory model for a deteriorating item incorporating learning effect using genetic algorithm,” Advances in Operations Research, vol. 2010, Article ID 146042, 26 pages, 2010.
[21]  B. Liu and K. Iwamura, “Chance constrained programming with fuzzy parameters,” Fuzzy Sets and Systems, vol. 94, no. 2, pp. 227–237, 1998.
[22]  A. Roy, M. K. Maiti, S. Kar, and M. Maiti, “An inventory model for a deteriorating item with displayed stock dependent demand under fuzzy inflation and time discounting over a random planning horizon,” Applied Mathematical Modelling, vol. 33, no. 2, pp. 744–759, 2009.
[23]  B. Das, K. Maity, and M. Maiti, “A two warehouse supply-chain model under possibility/ necessity/credibility measures,” Mathematical and Computer Modelling, vol. 46, no. 3-4, pp. 398–409, 2007.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413