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All issues Volume 55 / No 2 (March-April 2021) RAIRO-Oper. Res., 55 2 (2021) 571-588 References
Free Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 2, March-April 2021
Page(s) 571 - 588
DOI https://doi.org/10.1051/ro/2021021
Published online 31 March 2021
  • A.A. Alamri, Theory and methodology on the global optimal solution to a general reverse logistics inventory model for deteriorating items and time-varying rates. Comput. Ind. Eng. 60 (2011) 236–247. [Google Scholar]
  • E. Bazan, M.Y. Jaber and S. Zanoni, A review of mathematical inventory models for reverse logistics and the future of its modeling: An environmental perspective. Appl. Math. Model. 40 (2016) 4151–4178. [Google Scholar]
  • J.D. Blackburn, V.D.R. Guide, G.C. Souza and L.N. Van Wassenhove, Reverse supply chains for commercial returns. California Manage. Rev. 46 (2004) 6–22. [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, Fuzzy production inventory for fuzzy product quantity with triangular fuzzy number. Fuzzy Sets Syst. 107 (1999) 37–57. [Google Scholar]
  • L.H. Chen and L.H. Ouyang, Fuzzy inventory model for deteriorating items with permissible delay in payment. Appl. Math. Comput. 182 (2006) 711–726. [Google Scholar]
  • W. Chen, L. Wei and Y. Li, Fuzzy multicycle manufacturing/remanufacturing production decisions considering inflation and the time value of money. J. Cleaner Prod. 198 (2018) 1494–1502. [Google Scholar]
  • I. Dobos and K. Richter, A production/recycling model with stationary demand and return rates. Central Eur. J. Oper. Res. 11 (2003) 35–46. [Google Scholar]
  • I. Dobos and K. Richter, An extended production/recycling model with stationary demand and return rates. Int. J. Prod. Econ. 90 (2004) 311–323. [Google Scholar]
  • D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980). [Google Scholar]
  • A.M.A. El Saadany and M.Y. Jaber, A production/remanufacturing inventory model with price and quality dependant return rate. Comput. Ind. Eng. 58 (2010) 352–362. [Google Scholar]
  • T. Garai, D. Chakraborty and T.P. Roy, Fully fuzzy inventory model with price-dependent demand and time varying holding cost under fuzzy decision variables. J. Intell. Fuzzy Syst. 36 (2019) 3725–3738. [Google Scholar]
  • K.A. Halim, B.C. Giri and K.S. Chaudhuri, Fuzzy production planning models for an unreliable production system with fuzzy production rate and stochastic/fuzzy demand rate. Int. J. Ind. Eng. Comput. 2 (2011) 179–192. [Google Scholar]
  • P. Hasanov, M.Y. Jaber and S. Zolfaghari, Production remanufacturing and waste disposal model for a case of the cases of pure and partial backordering. Appl. Math. Modell. 36 (2012) 5249–5261. [Google Scholar]
  • P. Hasanov, M.Y. Jaber and N. Tahirov, Four-level closed loop supply chain with remanufacturing. Appl. Math. Modell. 66 (2019) 141–155. [Google Scholar]
  • M.Y. Jaber and A.M.A. El Saadany, The production, remanufacture and waste disposal model with lost sales. Int. J. Prod. Econ. 120 (2009) 115–124. [Google Scholar]
  • A. Kaufmann and M.M. Gupta, Introduction to Fuzzy Arithmetic Theory and Applications. Van Nostrand Reinhold, New York (1991). [Google Scholar]
  • M. Kumar, A novel weakest t-norm based fuzzy fault tree analysis through qualitative data processing and its application in system reliability evaluation. J. Intell. Syst. 29 (2018) 977–993. [Google Scholar]
  • M. Kumar, A novel weakest t-norm based fuzzy importance measure for fuzzy fault tree analysis of combustion engineering reactor protection system. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 27 (2019) 949–967. [Google Scholar]
  • M. Kumar, Measuring Pearson’s correlation coefficient of fuzzy numbers with different membership functions under weakest t-norm. Int. J. Data Anal. Tech. Strategies 12 (2020) 172–186. [Google Scholar]
  • N. Nahmias and H. Rivera, A deterministic model for a repairable item inventory system with a finite repair rate. Int. J. Prod. Res. 17 (1979) 215–221. [Google Scholar]
  • S. Pal, G.S. Mahapatra and G.P. Samanta, An EPQ model of ramp type demand with Weibull deterioration under inflation and finite horizon in crisp and fuzzy environment. Int. J. Prod. Econ. 156 (2014) 159–166. [Google Scholar]
  • V. Polotski, J.-P. Kenne and A. Gharbi, Production and setup policy optimization for hybrid manufacturing-remanufacturing systems. Int. J. Prod. Econ. 183 (2017) 322–333. [Google Scholar]
  • K. Richter, The EOQ repair and waste disposal model with variable setup numbers. Eur. J. Oper. Res. 95 (1996) 313–324. [Google Scholar]
  • K. Richter, The extended EOQ repair and waste disposal model. Int. J. Prod. Econ. 45 (1996) 443–447. [Google Scholar]
  • D.A. Schrady, A deterministic inventory model for repairable items. Nav. Res. Logistics Q. 14 (1967) 391–398. [Google Scholar]
  • C. Singh and S.R. Singh, Progressive trade credit policy in a supply chain with and without stock-out for supplier’s lead time under inflationary and fuzzy environment. Syst. Sci. Control Eng. 3 (2015) 284–299. [Google Scholar]
  • S.R. Singh and S. Sharma, A production reliable model for deteriorating products with random demand and inflation. Int. J. Syst. Sci. Oper. Logistics 4 (2017) 330–338. [Google Scholar]
  • S.R. Singh and S. Sharma, A partially backlogged supply chain model for deteriorating items under reverse logistics, imperfect production/remanufacturing and inflation. Int. J. Logistics Syst. Manage. 33 (2019) 221–255. [CrossRef] [Google Scholar]
  • S.R. Singh, S. Sharma and M. Kumar, Inventory models with multiple production and remanufacturing batches under shortages. Control Cybern. 45 (2016) 385–416. [Google Scholar]
  • S.R. Singh, S. Sharma and M. Kumar, A reverse logistics model for decaying items with variable production and remanufacturing incorporating learning effects. Int. J. Oper. Res. 38 (2020) 422–448. [CrossRef] [Google Scholar]
  • M. Vujošević, D. Petrović and R. Petrović, EOQ formula when inventory cost is fuzzy. Int. J. Prod. Econ. 45 (1996) 499–504. [Google Scholar]
  • D. Yadav, S.R. Singh and R. Kumari, Inventory model with learning effect and imprecise market demand under screening error. Opsearch 50 (2013) 418–432. [Google Scholar]
  • J.S. Yao, S.C. Chang and J.S. Su, Fuzzy inventory without backorder for fuzzy order quantity and fuzzy total demand quantity. Comput. Oper. Res. 27 (2000) 935–962. [Google Scholar]
  • L.A. Zadeh, Fuzzy sets. Inf. Control 8 (1965) 338–353. [Google Scholar]
  • H.J. Zimmermann, Fuzzy Set Theory and its Applications, 3rd edition. Kluwer Academic Publishers, Dordrecht (1996). [CrossRef] [Google Scholar]

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