References

1. Arrow K., et al. “Optimal inventory policy”. Econometrica 19 (1951).
2. Arrow K., et al. “Studies in the mathematical theory of inventory and production”. Stanford, Calif, Stanford University Press (1958).
3. Kenkel J L. “Introductory Statistics for Management and Economics”. Boston: Prindil, Weber and Schmidt (1981).
4. Kennedy JJ and Bush AJ. “An introduction to the design and Analysis of Experiments of Behavioral Research”. Newyork: University Press of America (1985).
5. Mehra S., et al. “Some comments on the validity of EOQ formula under inflationary conditions”. Decision Sciences 1 (1991): 206-212.
6. Debreceny RS and Gray G L. “Data mining journal entries for fraud detection: An exploratory study”. International Journal of Accounting Information Systems 11 (2010): 157-181.
7. Hesse MF and Jr JHC. “Fraud risk management: A small Business Perspective”. Business Horizons 59 (2016): 13-18.
8. Kanapickiene R and Grundiene Z. “The model of fraud detection in financial statements by means of financial ratios”. Procedia-Social and Behavioral Sciences 213 (2015): 321-327.
9. Kim Y J., et al. “Detecting financial misstatements with fraud intension using multi-class cost sensitive learning”. Expert Systems with Applications 62 (2016): 32-43.
10. Mu E and Carroll J. “Development of a fraud risk decision model for prioritizing fraud risk cases in manufacturing firms”. International Journal of Production Economics 173 (2016): 30-42.
11. West J and Bhattacharya M. “Intelligent Financial Fraud Detection: A Comprehensive Review”. Computers and Security 57 (2016): 47-66.
12. Burton L. “Audit Report Details Fraud at Leslie Fay”. The Wall Street Journal. March 28 (1995): B1.
13. Fahrmeir L and Tutz, G. “Multivariate Statistical Modeling Based on Generalized Linear Models”. Springer-Verlag: New York.
14. Angulo JM., et al. “Semiparametric statistical approaches for space-time process prediction”. Environmental and Ecological Statistics 5 (1998): 297-316.
15. Pruscha H and Gottlein A. “Regression analysis of forest inventory data with time and space dependencies”. Environmental and Ecological Statistics1 (2002): 43-56.
16. Besta P., et al. “The utilization of statistical methods in the area of inventory management”. Jesenik, Czech Republic, EU (2012).
17. Ayloo, S., et al. “Correlation between the beck depression inventory and bariatric surgical procedures”. Surgery for Obesity and Related Disease11 (2015): 637-642.
18. Iqbal Q., et al. “Statistical analysis of multi-criteria inventory classification models in the presence of forecast upsides”. Production and Manufacturing Research1 (2017): 15-39.
19. Mutschler W. “Higher-order statistics for DSGE models”. Econometrics and Statistics 6 (2018): 44-56.
20. De SK and Pal M. “An intelligent decision for a bi-objective inventory problem”. International Journal of System Science: Operation: Logistics1 (2016).
21. De SK and Sana SS. “Fuzzy order quantity inventory model with fuzzy shortage quantity and fuzzy promotional index”. Economic Modelling 31 (2013a): 351-358.
22. De SK and Sana SS. “Backlogging EOQ model for promotional effort and selling price sensitive demand- an intuitionistic fuzzy approach”. Annals of Operations Research. Advance Online Publication 233 (2015): 57-76.
23. De SK and Sana SS. “An EOQ model with backlogging”. International Journal of Management Sciences and Engineering Management (2015).
24. De SK and Sana SS. “The (p, q, r, l) model for stochastic demand under intuitionistic fuzzy aggregation with Bonferroni mean”. Journal of Intelligent Manufacturing 29 (2018): 1753-1771.
25. De SK and Mahata GC. “Decision of a fuzzy inventory with fuzzy backorder model under cloudy fuzzy demand rate”. International Journal of Applied and Computational Mathematics 3 (2017): 2593-2609.
26. De S K and Mahata GC. “A cloudy fuzzy economic order quantity model for imperfect-quality items with allowable proportionate discounts”. Journal of Industrial Engineering International (2019a).
27. De S K and Mahata G C. “A comprehensive study of an economic order quantity model under fuzzy monsoon demand”. Sadhana (2019b).
28. Maity S., et al. “A comprehensive study of a backlogging EOQ model with nonlinear heptagonal dense fuzzy environment”. RAIRO-operations Research (2018a).
29. Maity S., et al. “A study of a back order EOQ model for cloud type intuitionistic dense fuzzy demand rate”. International Journal of Fuzzy System 22 (2019b): 201-211.
30. Karmakar S., et al. “A pollution sensitive dense fuzzy economic production quantity model with cycle time dependent production rate”. Journal of Cleaner Production 154 (2017): 139-150.
31. Karmakar S., et al. “A pollution sensitive remanufacturing model with waste items: Triangular dense fuzzy lock set approach”. Journal of Cleaner Production 187 (2018): 789-803.
32. Maity S., et al. “A Study of an EOQ Model under Lock Fuzzy Environment”. Mathematics1 (2019a).
33. Maity S., et al. “Two Decision Makers’ Single Decision over a Back Order EOQ Model With Dense Fuzzy Demand Rate”. Finance and Market 3 (2018b): 1-11.
34. Chakraborty A., et al. “Hexagonal fuzzy number and its distinctive representation, ranking, defuzzification technique and application in production inventory management problem”. Granular Computing 6 (2020): 507-521.
35. Nobil A M., et al. “Reorder point for the EOQ inventory model with imperfect quality items”. Ain Shams Engineering Journal4 (2020): 1339-1343.
36. Khan M A., et al. “Inventory models for perishable items with advanced payment, linearly time-dependent holding cost and demand dependent on advertisement and selling price”. International Journal of Production Economics 230 (2020): 107804.
37. Rahman MS., et al. “Necessary and Sufficient Optimality Conditions for non-linear Unconstrained and Constrained optimization problem with Interval valued objective function”. Computers and Industrial Engineering147 (2020).
38. Maity S., et al. “A study of an EOQ model with public-screened discounted items under cloudy fuzzy demand rate”. Journal of Intelligent and Fuzzy System6 (2021): 6923-6934.
39. Maity S., et al. “A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate”. Applied System Innovation 4 (2021): 81.
40. Maity S., et al. “Validity Judgement of an EOQ Model using Phi-coefficient, Optimal Decision Making in Operations Research and Statistics: Methodologies and Applications”. CRC Press (2021): 378-387.
41. Maity S. “A Study of a Back Order EOQ Model Using Uncertain Demand Rate”. Acta Scientific Computer Sciences (2020): 1-7.

Citation

Citation: Pratima Dinda and Suman Maity. “Validity Judgment of an EOQ Model Under Customers’ Feedback". Acta Scientific Computer Sciences 4.11 (2022): 46-55 .

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Copyright: © 2022 Pratima Dinda and Suman Maity. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.