EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 55 / No 2 (March-April 2021) RAIRO-Oper. Res., 55 2 (2021) 723-744 References
Free Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 2, March-April 2021
Page(s) 723 - 744
DOI https://doi.org/10.1051/ro/2021020
Published online 31 March 2021
  • T. Allahviranloo and R. Saneifard, Defuzzification method for ranking fuzzy numbers based on center of gravity. Iran. J. Fuzzy Syst. 9 (2012) 57–67. [Google Scholar]
  • R.E. Bellman and L.A. Zadeh, Decision making in a fuzzy environment. Manage. Sci. 17 (1970) B141–B164. [Google Scholar]
  • R. Cerulli, C. D’Ambrosio and M. Gentili, Best and worst values of the optimal cost of the interval transportation problem. ODS 2017: Optimization and Decision Science: Methodologies and Applications. In Vol. 217 of Springer Proceedings in Mathematics & Statistics. Springer (2017) 367–374. [Google Scholar]
  • H.C. Chang, An application of fuzzy sets theory to the EOQ model with imperfect quality items. Comput. Oper. Res. 31 (2004) 2079–2092. [Google Scholar]
  • S.C. Chang, J.S. Yao and H.M. Lee, Economic reorder point for fuzzy backorder quantity. Eur. J. Oper. Res. 109 (1998) 183–202. [Google Scholar]
  • S.H. Chen and C.H. Hsieh, Graded mean integration representation of generalized fuzzy number. J. Chin. Fuzzy Syst. Assoc. 5 (1999) 1–7. [Google Scholar]
  • C. D’Ambrosio, R. Cerulli and M. Gentili, The optimal value range problem for the interval (immune) transportation problem. Omega 95 (2020) 102059. [Google Scholar]
  • P. Das, S.K. De and S.S. Sana, An EOQ model for time dependent backlogging over idle time: a step order fuzzy approach. Int. J. Appl. Comput. Math. 1 (2014) 1–17. [Google Scholar]
  • S.K. De and I. Beg, Triangular dense fuzzy sets and new defuzzification methods. J. Intell. Fuzzy Syst. 31 (2016) 469–477. [Google Scholar]
  • S.K. De and I. Beg, Triangular dense fuzzy Neutrosophic sets. Neutrosophic Sets Syst. 13 (2016) 1–12. [Google Scholar]
  • S.K. De and G.C. Mahata, Decision of a fuzzy inventory with fuzzy backorder model under cloudy fuzzy demand rate. Int. J. Appl. Comput. Math. 3 (2017) 2593–2609. [Google Scholar]
  • S.K. De and G.C. Mahata, A production-inventory model with imperfect production process and partial backlogging under learning considerations in fuzzy random environments. J. Intell. Manuf. 28 (2017) 883–897. [Google Scholar]
  • S.K. De and G.C. Mahata, A comprehensive study of an economic order quantity model under fuzzy monsoon demand. Sadhana 44 (2019) 89–101. [Google Scholar]
  • S.K. De and G.C. Mahata, A cloudy fuzzy economic order quantity model for imperfect-quality items with allowable proportionate discounts. Int. J. Ind. Eng. 15 (2019) 571–583. [Google Scholar]
  • S.K. De and G.C. Mahata, An EPQ model for three-layer supply chain with partial backordering and disruption: triangular dense fuzzy lock set approach. Sadhana 44 (2019) 177–192. [Google Scholar]
  • S.K. De and G.C. Mahata, A production inventory supply chain model with partial backordering and disruption under triangular linguistic dense fuzzy lock set approach. Soft Comput. 24 (2020) 5053–5069. [Google Scholar]
  • S.K. De and S.S. Sana, Fuzzy order quantity inventory model with fuzzy shortage quantity and fuzzy promotional index. 31 Econ. Model. (2013) 351–358. [Google Scholar]
  • S.K. De and S.S. Sana, Backlogging EOQ model for promotional effort and selling price sensitive demand – an intuitionistic fuzzy approach. Ann. Oper. Res. 233 (2015) 57–76. [Google Scholar]
  • S.K. De and S.S. Sana, The (p, q, r, l) model for stochastic demand under Intuitionistic fuzzy aggregation with Bonferroni mean. J. Intell. Manuf. 29 (2018) 1753–1771. [Google Scholar]
  • S.K. De, P.K. Kundu and A. Goswami, An economic production quantity inventory model involving fuzzy demand rate and fuzzy deterioration rate. J. Appl. Math. Comput. 12 (2003) 251–260. [Google Scholar]
  • H. Deng, Comparing and ranking fuzzy numbers using ideal solutions. Appl. Math. Model. 38 (2014) 1638–1646. [Google Scholar]
  • R. Ezzati, T. Allahviranloo, S. Khezerloo and M. Khezerloo, An approach for ranking of fuzzy numbers. Expert Syst. App. 39 (2012) 690–695. [Google Scholar]
  • T. Hajjari and S. Abbasbandy, A note on “The revised method of ranking LR fuzzy number based on deviation degree”. Expert Syst. App. 39 (2011) 13491–13492. [Google Scholar]
  • P.A. Hayek and M.K. Salameh, Production lot sizing with the reworking of imperfect quality items produced. Prod. Plan. Control 12 (2001) 584–590. [Google Scholar]
  • M. Karimi-Nasab and K. Sabri-Laghaie, Developing approximate algorithms for EPQ problem with process compressibility and random error in production/inspection. Int. J. Prod. Res. 52 (2014) 2388–2421. [Google Scholar]
  • N. Kazemi, E. Shekarian, L.E. Cárdenas-Barrón and E.U. Olugu, Incorporating human learning into a fuzzy EOQ inventory model with backorders. Comput. Ind. Eng. 87 (2015) 540–542. [Google Scholar]
  • N. Kazemi, E.U. Olugu, A.R. Salwa Hanim and R.A.B.R. Ghazilla, Development of a fuzzy economic order quantity model for imperfect quality items using the learning effect on fuzzy parameters. J. Intell. Fuzzy Syst. 28 (2015) 2377–2389. [CrossRef] [Google Scholar]
  • N. Kazemi, E.U. Olugu, A.R. Salwa Hanim and R.A.B.R. Ghazilla, A fuzzy EOQ model with backorders and forgetting effect on fuzzy parameters: an empirical study. Comput. Ind. Eng. 96 (2016) 140–148. [Google Scholar]
  • A. Kumar, P. Singh, P. Kaur and A. Kaur, A new approach for ranking of L-R type generalized fuzzy numbers. Expert Syst. App. 38 (2011) 10906–10910. [Google Scholar]
  • B. Maddah, M.K. Salameh and L. Moussawi-Haidar, Order overlapping: a practical approach for preventing shortages during screening. Comput. Ind. Eng. 58 (2010) 691–695. [Google Scholar]
  • G.C. Mahata, A production-inventory model with imperfect production process and partial backlogging under learning considerations in fuzzy random environments. J. Intel. Manuf. 28 (2017) 883–897. [Google Scholar]
  • G.C. Mahata and A. Goswami, An EOQ model for deteriorating items under trade credit financing in the fuzzy sense. Prod. Plan. Control 18 (2007) 681–692. [CrossRef] [Google Scholar]
  • G.C. Mahata and A. Goswami, Fuzzy inventory models for items with imperfect quality and shortage backordering under crisp and fuzzy decision variables. Comput. Ind. Eng. 64 (2013) 190–199. [Google Scholar]
  • G.C. Mahata and P. Mahata, Analysis of a fuzzy economic order quantity model for deteriorating items under retailer partial trade credit financing in a supply chain., Math. Comput. Model. 53 (2011) 1621–1636. [Google Scholar]
  • G.C. Mahata, A. Goswami and D.K. Gupta, A joint economic-lot-size model for purchaser and vendor in fuzzy sense. Comput. Math. App. 50 (2005) 1767–1790. [Google Scholar]
  • L. Moussawi-Haidar, M. Salameh and W. Nasr, An instantaneous replenishment model under the effect of a sampling policy for defective items. Appl. Math. Modell. 37 (2013) 719–727. [Google Scholar]
  • L. Moussawi-Haidar, M. Salameh and W. Nasr, Effect of deterioration on the instantaneous replenishment model with imperfect quality items. Appl. Math. Modell. 38 (2014) 5956–5966. [Google Scholar]
  • S. Papachristos and I. Konstantaras, Economic ordering quantity models for items with imperfect quality. Int. J. Prod. Econ. 100 (2006) 148–154. [Google Scholar]
  • K.S. Park, Fuzzy-set theoretic interpretation of economic order quantity. IEEE Transactions on Systems, Man, and Cybernetics SMC-17 (1987) 1082–1084. [Google Scholar]
  • E.L. Porteus, Optimal lot sizing, process quality improvement and setup cost reduction. Oper. Res. 34 (1986) 137–144. [Google Scholar]
  • M.J. Rosenblatt and H.L. Lee, Economic production cycles with imperfect production processes. IIE Trans. 18 (1986) 48–55. [Google Scholar]
  • S.M. Ross, Introduction to Probability Models. Academic Press, New York (1993). [Google Scholar]
  • M.K. Salameh and M.Y. Jaber, Economic production quantity model for items with imperfect quality. Int. J. Prod. Econ. 64 (2000) 59–64. [Google Scholar]
  • R.L. Schwaller, EOQ under inspection costs. Prod. Inventory Manage. J. 29 (1988) 22–24. [Google Scholar]
  • W. Shih, Optimal inventory policies when stockouts result from defective products. Int. J. Prod. Res. 18 (1980) 677–685. [Google Scholar]
  • E.A. Silver, Establishing the reorder quantity when amount received is uncertain. INFOR 14 (1976) 32–39. [Google Scholar]
  • G. Sommer, Fuzzy inventory scheduling. In: Applied Systems and Cybernetics. Pergamon Press, New York (1981) 3052–3060. [Google Scholar]
  • M.I.M. Wahab and M.Y. Jaber, Economic order quantity model for items with imperfect quality, different holding costs, and learning effects: a note. Comput. Ind. Eng. 58 (2010) 186–190. [Google Scholar]
  • Z.X. Wang, Y.J. Liu, Z.P. Fan and B. Feng, Ranking L-R fuzzy number based on deviation degree. Inf. Sci. 179 (2009) 2070–2077. [Google Scholar]
  • I.P. Wright, Factors affecting the cost of airplanes. J. Aeronautic Sci. 3 (1936) 122–128. [Google Scholar]
  • P. Xu, X. Su, J. Wu, X. Sun, Y. Zhang and Y. Deng, A note on ranking generalized fuzzy numbers. Expert Syst. App. 39 (2012) 6454–6457. [Google Scholar]
  • R.R. Yager, A procedure for ordering fuzzy subsets of the unit interval. Inf. Sci. 24 (1981) 143–161. [Google Scholar]
  • H.F. Yu, W.K. Hsu and W.J. Chang, EOQ model where a portion of the defectives can be used as perfect quality. Int. J. Syst. Sci. 43 (2012) 1689–1698. [Google Scholar]
  • V.F. Yu, H.T.X. Chi, L.Q. Dat, P.N.K. Phuc and C.W. Shen, Ranking generalized fuzzy numbers in fuzzy decision making based on the left and right transfer coefficients and areas. Appl. Math. Model. 37 (2013) 8106–8117. [Google Scholar]
  • L.A. Zadeh, Fuzzy sets. Inf. Control 8 (1965) 338–356. [Google Scholar]
  • X.I.N. Zhang and Y. Gerchak, Joint lot sizing and inspection policy in an EOQ model with random yield. IIE Trans. 22 (1990) 41–47. [Google Scholar]
  • F. Zhang, J. Ignatius, C.P. Lim and Y. Zhao, A new method for ranking fuzzy numbers and its application to group decision making. Appl. Math. Modell. 38 (2014) 1563–1582. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.