Free Access
Issue
RAIRO-Oper. Res.
Volume 54, Number 1, January-February 2020
Page(s) 267 - 286
DOI https://doi.org/10.1051/ro/2018114
Published online 11 February 2020
  • A. Felix and A.V. Devadoss, A new decagonal fuzzy number under uncertain linguistic environment. Int. J. Math. App. 3 (2015) 9–97. [Google Scholar]
  • A.S. Sudha and S. Karunambigai, Solving a transportation problem using a Heptagonal fuzzy number. Int. J. Adv. Res. Sci. Eng. Technol. 4 (2017) 3118–3115. [Google Scholar]
  • B. Asady, The revised method of ranking LR fuzzy number based on deviation Degree. Expert Syst. App. 37 (2010) 5056–5060. [CrossRef] [Google Scholar]
  • C.H. Cheng, A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets Syst. 95 (1998) 307–317. [CrossRef] [Google Scholar]
  • D.P. Filev and R.R. Yager, A generalized defuzzification method via BADD Distributions. Int. J. Intell. Syst. 6 (1991) 687–697. [CrossRef] [Google Scholar]
  • E. Shekarian, E.U. Olugu, S.H. Abdul-Rashid and E. Bottani, A fuzzy reverse logistics inventory system integrating economic order/production quantity models. Int. J. Fuzzy Syst. 18 (2016) 1141–1161. [CrossRef] [Google Scholar]
  • E. Shekarian, E.U. Olugu, S.H. Abdul-Rashid and N. Kazemi, Analyzing optimization techniques in inventory models: the case of fuzzy economic order quantity problems. In: Int. Conference on Industrial Engineering and Operations Management. Kuala Lumpur, Malaysia, March 8-10 (2016) 1229–1240. [Google Scholar]
  • E. Shekarian, E.U. Olugu, S.H. Abdul-Rashid and N. Kazemi, An economic order quantity model considering different holding costs for imperfect quality items subject to fuzziness and learning. J. Intell. Fuzzy Syst. 30 (2016) 2985–2997. [CrossRef] [Google Scholar]
  • E. Halgamuge, N. Kazemi, S.H. Abdul-Rashid and E.U. Olugu, Fuzzy inventory models: a comprehensive review. Appl. Soft Comput. 55 (2017) 588–621. [Google Scholar]
  • K. Rathi and S. Balamohan, A mathematical model for subjective evaluation of alternatives in Fuzzy multi-criteria group decision making using COPRAS method. Int. J. Fuzzy Syst. 19 (2017) 1290–1299. [CrossRef] [Google Scholar]
  • K. Rathi and S. Balamohan, Comparative study of arithmetic nature of Heptagonal fuzzy numbers. Appl. Math. Sci. 8 (2016) 4309–4321. [Google Scholar]
  • K. Rathi, S. Balamohan, M. Revathi and B. Ananthi, A fuzzy approach for unequal workers-task assignment with heptagonal fuzzy numbers. Int. J. Recent Innov. Trends Comput. Commun. 4 (2016) 564–569. [Google Scholar]
  • L.A. Zadeh, Fuzzy sets. Inf. Control 8 (1965) 338–353. [CrossRef] [MathSciNet] [Google Scholar]
  • L.H. Chen and H.W. Lu, An approximate approach for ranking fuzzy numbers based on left and right dominance. Comput. Math. Appl. 41 (2001) 1589–1602. [Google Scholar]
  • L.H. Chen and H.W. Lu, The preference order of fuzzy numbers. Comput. Math. Appl. 44 (2002) 1455–1465. [Google Scholar]
  • N.I. Namarta, N. Thakur and U.C. Gupta, Ranking of heptagonal fuzzy numbers using incentre of centroids. Int. J. Adv. Technol. Eng. Sci. 5 (2017) 248–255. [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, A fuzzy EOQ model with backorders and forgetting effect on fuzzy parameters: an emperical study. Comput. Ind. Eng. 96 (2016) 140–148. [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]
  • 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]
  • Q. Song and R.P. Leland, Adaptive learning defuzzification techniques and applications. Comput. Math. Appl. 81 (1996) 321–329. [Google Scholar]
  • R. Patro, M. Acharya, M.M. Nayak and S. Patnaik, A fuzzy EOQ model for deteriorating items with imperfect quality using proportionate discount under learning effects. Int. J. Manage. Decis. Making 17 (2018) https://doi.org/10.1504/IJMDM.2018.092557 [Google Scholar]
  • R.R. Yager, Knowledge-based defuzzification. Fuzzy Sets Syst. 80 (1996) 177–185. [CrossRef] [Google Scholar]
  • S. Abbasbandy and B. Asady, Ranking of fuzzy numbers by sign distance. Inf. Sci. 176 (2006) 2405–2416. [Google Scholar]
  • S. Abbasbandy and T. Hajjari, A new approach for ranking of trapezoidal fuzzy Numbers. Comput. Math. Appl. 57 (2009) 413–419. [Google Scholar]
  • S. Abbasbandy and T. Hajjari, An improvement on centroid point method for ranking of fuzzy numbers. J. Sci. I.A.U. 78 (2011) 109–119. [Google Scholar]
  • S. Halgamuge, T. Runkler and M. Glesner, On the neural defuzzification Methods. In: Proceeding of the 5th IEEE International Conference on Fuzzy Systems (1996) 463–469. [Google Scholar]
  • S.J. Chen and S.M. Chen, A new method for handling multicriteria fuzzy decision making problems using FN-IOWA operators. Cybern. Systems. 34 (2003) 109–137. [CrossRef] [Google Scholar]
  • S.J. Chen and S.M. Chen, Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Appl. Intell. 26 (2007) 1–11. [CrossRef] [Google Scholar]
  • S. Karmakar, S.K. De and A. Goswami, A pollution sensitive dense fuzzy economic production quantity model with cycle time dependent production rate. J. Cleaner Prod. 154 (2017) 139–150. [CrossRef] [Google Scholar]
  • S. Karmakar, S.K. De and A. Goswami, A pollution sensitive remanufacturing model with waste items: triangular dense fuzzy lock set approach. J. Cleaner Prod. 187 (2018) 789–803. [CrossRef] [Google Scholar]
  • S.K. De, A. Goswami and S.S. Sana, An interpolating by pass to Pareto optimality in intuitionistic fuzzy technique for a EOQ model with time sensitive backlogging. App. Math. Comput. 230 (2014) 664–674. [CrossRef] [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. [CrossRef] [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 I. Beg, Triangular dense fuzzy sets and new defuzzication methods. J. Intell. Fuzzy Syst. 31 (2016) 467–479. [Google Scholar]
  • S.K. De and S.S. Sana, An EOQ model with backlogging. Int. J. Manage. Sci. Eng. Manage. 11 (2016) 143–154. [Google Scholar]
  • S.K. De and S.S. Sana, An alternative fuzzy EOQ model with backlogging for selling price and promotional effort sensitive demand. Int. J. Appl. Comput. Math. 1 (2015) 69–86. [CrossRef] [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 (2013) 57–76. [Google Scholar]
  • S.K. De and S.S. Sana, Fuzzy order quantity inventory model with fuzzy shortage quantity and fuzzy promotional index. Econ. Model. 31 (2013) 351–358. [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, EOQ model with natural idle time and wrongly measured demand rate. Int. J. Inventory Control Manage. 3 (2013) 329–354. [Google Scholar]
  • S.K. De, Triangular dense fuzzy lock set. Soft Comput. 22 (2018) 7243–7254. [Google Scholar]
  • S.K. Sharma and S.M. Govindaluri, An analytical approach for EOQ determination using trapezoidal fuzzy function. Int. J. Procurement Manage. 11 (2018) 356–369. [CrossRef] [Google Scholar]
  • S. Maity, S.K. De and M. Pal, Two decision makers’ single decision over a back order EOQ model with dense fuzzy demand rate. Finance Market 3 (2018) 1–11. [CrossRef] [Google Scholar]
  • S.M. Chen and J.H. Chen, Fuzzy risk analysis based on the ranking of generalized fuzzy numbers with different heights and different spreads. Expert Syst. App. 36 (2009) 6833–6842. [CrossRef] [Google Scholar]
  • S. Selvakumari and S. Lavanya, Fuzzy game problem with payoffs as linguistic variables. Int. J. Eng. Sci. Manage. Res. 3 (2016) 18–24. [Google Scholar]
  • S.S.L. Chang and L.A. Zadeh, On fuzzy mappings and control. IEEE Trans. Syst. Man Cybern. 2 (1972) 30–34. [Google Scholar]
  • T. Chu and C. Tsao, Ranking fuzzy numbers with an area between the centroid point and original point. Comput. Math. Appl. 43 (2002) 111–117. [Google Scholar]
  • T. Jiang and Y. Li, Generalized defuzzification strategies and their parameter learning procedure. IEEE Trans. Fuzzy Syst. 4 (1996) 64–71. [Google Scholar]
  • T. Hajjari, On deviation degree methods for ranking fuzzy numbers. Aust. J. Basic App. Sci. 5 (2011) 750–758. [Google Scholar]
  • T. Hajjari, Ranking of fuzzy numbers based on ambiguity degree. Aust. J. Basic App. Sci. 5 (2011) 62–69. [Google Scholar]
  • U. Chanda and A. Kumar, Optimisation of fuzzy EOQ model for advertising and price sensitive demand model under dynamic ceiling on potential adoption. Int. J. Syst. Sci.: Oper. Logist. 4 (2017) 145–165. [Google Scholar]
  • X.W. Liu andS.L. Han, Ranking fuzzy numbers with preference weighting function expectation. Comput. Math. Appl. 49 (2005) 1455–1465. [Google Scholar]
  • Y. Deng and Q. Liu, A TOPSIS-based centroid index ranking method of fuzzy numbers and its application in decision-making. Cybern. Syst. 36 (2005) 581–595. [Google Scholar]
  • Y. Deng, Z.F. Zhu and Q. Liu, Ranking fuzzy numbers with an area method using of gyration. Comput. Math. Appl. 51 (2006) 1127–1136. [Google Scholar]
  • Y.J. Wang and H.S. Lee, The revised method of ranking fuzzy numbers with an area between the centroid and original points. Comput. Math. Appl. 55 (2008) 2033–2042. [Google Scholar]
  • Z.X. Wang, Y.J. Liu, Z.P. Fan and B. Feng, Ranking L-R fuzzy numbers based on deviation degree. Inf. Sci. 176 (2009) 2070–2077. [Google Scholar]

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