Open Access
Issue |
RAIRO-Oper. Res.
Volume 55, Number 6, November-December 2021
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Page(s) | 3715 - 3742 | |
DOI | https://doi.org/10.1051/ro/2021174 | |
Published online | 17 December 2021 |
- D. Chakraborty and S.K. Bhuiya, A continuous review inventory model with fuzzy service level constraint and fuzzy random variable parameters. Int. J. Appl. Comput. Math. 3 (2017) 3159–3174. [CrossRef] [MathSciNet] [Google Scholar]
- D. Chakraborty, D. Guha and B. Dutta, Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization. Soft Comput. 20 (2016) 2245–2259. [Google Scholar]
- C.K. Chan, W.H. Wong, A. Langevin and Y. Lee, An integrated production-inventory model for deteriorating items with consideration of optimal production rate and deterioration during delivery. Int. J. Prod. Econ. 189 (2017) 1–13. [CrossRef] [Google Scholar]
- H.-C. Chang, J.-S. Yao and L.-Y. Ouyang, Fuzzy mixture inventory model involving fuzzy random variable lead time demand and fuzzy total demand. Eur. J. Oper. Res. 169 (2006) 65–80. [CrossRef] [Google Scholar]
- Z. Chen, Optimization of production inventory with pricing and promotion effort for a single-vendor multi-buyer system of perishable products. Int. J. Prod. Econ. 203 (2018) 333–349. [CrossRef] [Google Scholar]
- S.-M. Chen and L.-W. Lee, Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. Expert Syst. App. 37 (2010) 824–833. [CrossRef] [Google Scholar]
- T.-Y. Chen, C.-H. Chang and J.-F.R. Lu, The extended qualiflex method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making. Eur. J. Oper. Res. 226 (2013) 615–625. [CrossRef] [Google Scholar]
- X. Chen, S. Benjaafar and A. Elomri, The carbon-constrained EOQ. Oper. Res. Lett. 41 (2013) 172–179. [CrossRef] [MathSciNet] [Google Scholar]
- M. Cococcioni, P. Ducange, B. Lazzerini and F. Marcelloni, A pareto-based multi-objective evolutionary approach to the identification of mamdani fuzzy systems. Soft Comput. 11 (2007) 1013–1031. [CrossRef] [Google Scholar]
- S. Coupland and R. John, A fast geometric method for defuzzification of type-2 fuzzy sets. IEEE Trans. Fuzzy Syst. 16 (2008) 929–941. [CrossRef] [Google Scholar]
- S.K. De and A. Goswami, A replenishment policy for items with finite production rate and fuzzy deterioration rate. OPSEARCH 38 (2001) 419–430. [CrossRef] [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. [CrossRef] [MathSciNet] [Google Scholar]
- O. Dey and D. Chakraborty, A fuzzy random continuous review inventory system. Int. J. Prod. Econ. 132 (2011) 101–106. [CrossRef] [Google Scholar]
- O. Dey and D. Chakraborty, A fuzzy random periodic review system with variable lead-time and negative exponential crashing cost. Appl. Math. Model. 36 (2012) 6312–6322. [CrossRef] [MathSciNet] [Google Scholar]
- B.K. Dey, B. Sarkar, M. Sarkar and S. Pareek, An integrated inventory model involving discrete setup cost reduction, variable safety factor, selling price dependent demand, and investment. RAIRO-Oper. Res. 53 (2019) 39–57. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- D. Dubois and H. Prade, Operations on fuzzy numbers. Int. J. Syst. Sci. 9 (1978) 613–626. [Google Scholar]
- P. Dutta, D. Chakraborty and A.R. Roy, A single-period inventory model with fuzzy random variable demand. Math. Comput. Model. 41 (2005) 915–922. [CrossRef] [Google Scholar]
- P. Dutta, D. Chakraborty and A. Roy, Continuous review inventory model in mixed fuzzy and stochastic environment. Appl. Math. Comput. 188 (2007) 970–980. [MathSciNet] [Google Scholar]
- P. Ghare, A model for an exponentially decaying inventory. J. Ind. Eng. 14 (1963) 238–243. [Google Scholar]
- S. Greenfield and F. Chiclana, Type-reduced set structure and the truncated type-2 fuzzy set. Fuzzy Sets Syst. 352 (2018) 119–141. [CrossRef] [Google Scholar]
- S. Greenfield, F. Chiclana, S. Coupland and R. John, The collapsing method of defuzzification for discretised interval type-2 fuzzy sets. Information Sciences 179 (2009) 2055–2069. [CrossRef] [MathSciNet] [Google Scholar]
- S. Greenfield, F. Chiclana, R. John and S. Coupland, The sampling method of defuzzification for type-2 fuzzy sets: experimental evaluation. Inf. Sci. 189 (2012) 77–92. [CrossRef] [Google Scholar]
- P. Grzegorzewski, Nearest interval approximation of a fuzzy number. Fuzzy Sets Syst. 130 (2002) 321–330. [CrossRef] [Google Scholar]
- V. Hovelaque and L. Bironneau, The carbon-constrained EOQ model with carbon emission dependent demand. Int. J. Prod. Econ. 164 (2015) 285–291. [Google Scholar]
- T. Jia, Y. Liu, N. Wang and F. Lin, Optimal production-delivery policy for a vendor–buyers integrated system considering postponed simultaneous delivery. Comput. Ind. Eng. 99 (2016) 1–15. [CrossRef] [Google Scholar]
- N.N. Karnik and J.M. Mendel, Centroid of a type-2 fuzzy set. Inf. Sci. 132 (2001) 195–220. [CrossRef] [Google Scholar]
- A. Khanna, P. Gautam, B. Sarkar and C.K. Jaggi, Integrated vendor–buyer strategies for imperfect production systems with maintenance and warranty policy. RAIRO-Oper. Res. 54 (2020) 435–450. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- R.S. Kumar and A. Goswami, A continuous review production–inventory system in fuzzy random environment: minmax distribution free procedure. Comput. Ind. Eng. 79 (2015) 65–75. [CrossRef] [Google Scholar]
- R.S. Kumar, M. Tiwari and A. Goswami, Two-echelon fuzzy stochastic supply chain for the manufacturer-buyer integrated production-inventory system. J. Intell. Manuf. 27 (2016) 875–888. [CrossRef] [Google Scholar]
- Y.-P. Lee and C.-Y. Dye, An inventory model for deteriorating items under stock-dependent demand and controllable deterioration rate. Comput. Ind. Eng. 63 (2012) 474–482. [CrossRef] [Google Scholar]
- J. Li, R. John, S. Coupland and G. Kendall, On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets. IEEE Trans. Fuzzy Syst. 26 (2017) 1036–1039. [Google Scholar]
- G. Li, X. He, J. Zhou and H. Wu, Pricing, replenishment and preservation technology investment decisions for non-instantaneous deteriorating items. Omega 84 (2019) 114–126. [CrossRef] [Google Scholar]
- Y. Liang and F. Zhou, A two-warehouse inventory model for deteriorating items under conditionally permissible delay in payment. Appl. Math. Model. 35 (2011) 2221–2231. [Google Scholar]
- S.-T. Lo, H.-M. Wee and W.-C. Huang, An integrated production-inventory model with imperfect production processes and weibull distribution deterioration under inflation. Int. J. Prod. Econ. 106 (2007) 248–260. [CrossRef] [Google Scholar]
- X. Ma, P. Wu, L. Zhou, H. Chen, T. Zheng and J. Ge, Approaches based on interval type-2 fuzzy aggregation operators for multiple attribute group decision making. Int. J. Fuzzy Syst. 18 (2016) 697–715. [CrossRef] [MathSciNet] [Google Scholar]
- J.M. Mendel and X. Liu, New closed-form solutions for karnik-mendel algorithm+ defuzzification of an interval type-2 fuzzy set. In: 2012 IEEE International Conference on Fuzzy Systems. IEEE (2012) 1–8. [Google Scholar]
- D.J. Mohanty, R.S. Kumar and A. Goswami, A two-warehouse inventory model for non-instantaneous deteriorating items over stochastic planning horizon. J. Ind. Prod. Eng. 33 (2016) 516–532. [Google Scholar]
- D.J. Mohanty, R.S. Kumar and A. Goswami, Vendor-buyer integrated production-inventory system for imperfect quality item under trade credit finance and variable setup cost. RAIRO-Oper. Res. 52 (2018) 1277–1293. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- J.E. Moreno, M.A. Sanchez, O. Mendoza, A. Rodrguez-Daz, O. Castillo, P. Melin and J.R. Castro, Design of an interval type-2 fuzzy model with justifiable uncertainty. Inf. Sci. 513 (2020) 206–221. [CrossRef] [Google Scholar]
- M. Nie and W.W. Tan, Towards an efficient type-reduction method for interval type-2 fuzzy logic systems. In: Fuzzy Systems, 2008. FUZZ-IEEE 2008 (IEEE World Congress on Computational Intelligence). IEEE International Conference on Fuzzy Systems. IEEE (2008) 1425–1432. [Google Scholar]
- L.-Y. Ouyang, K.-S. Wu and C.-T. Yang, A study on an inventory model for non-instantaneous deteriorating items with permissible delay in payments. Comput. Ind. Eng. 51 (2006) 637–651. [CrossRef] [Google Scholar]
- B. Pal, A. Mandal and S.S. Sana, Two-phase deteriorated supply chain model with variable demand and imperfect production process under two-stage credit financing. RAIRO-Oper. Res. 55 (2021) 457–480. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- V. Pando, L.A. San-José, J. Garca-Laguna and J. Sicilia, Optimal lot-size policy for deteriorating items with stock-dependent demand considering profit maximization. Comput. Ind. Eng. 117 (2018) 81–93. [CrossRef] [Google Scholar]
- H. Rau, M.-Y. Wu and H.-M. Wee, Integrated inventory model for deteriorating items under a multi-echelon supply chain environment. Int. J. Prod. Econ. 86 (2003) 155–168. [CrossRef] [Google Scholar]
- C. Rout, R.S. Kumar, D. Chakraborty and A. Goswami, An EPQ model for deteriorating items with imperfect production, inspection errors, rework and shortages: a type-2 fuzzy approach. OPSEARCH 56 (2019) 657–688. [CrossRef] [MathSciNet] [Google Scholar]
- C. Rout, A. Paul, R.S. Kumar, D. Chakraborty and A. Goswami, Cooperative sustainable supply chain for deteriorating item and imperfect production under different carbon emission regulations. J. Cleaner Prod. 272 (2020) 122170. [CrossRef] [Google Scholar]
- C. Rout, D. Chakraborty and A. Goswami, An EPQ model for deteriorating items with imperfect production, two types of inspection errors and rework under complete backordering. Int. Game Theory Rev. 22 (2020) 2040011. [CrossRef] [MathSciNet] [Google Scholar]
- C. Rout, D. Chakraborty and A. Goswami, A production inventory model for deteriorating items with backlog-dependent demand. RAIRO-Oper. Res. 55 (2021) S549–S570. [CrossRef] [EDP Sciences] [Google Scholar]
- T.A. Runkler, C. Chen and R. John, Type reduction operators for interval type-2 defuzzification. Inf. Sci. 467 (2018) 464–476. [CrossRef] [Google Scholar]
- B. Sarkar, B.K. Dey, M. Sarkar, S. Hur, B. Mandal and V. Dhaka, Optimal replenishment decision for retailers with variable demand for deteriorating products under a trade-credit policy. RAIRO-Oper. Res. 54 (2020) 1685–1701. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- S. Sarkar, B.C. Giri and A.K. Sarkar, A vendor–buyer inventory model with lot-size and production rate dependent lead time under time value of money. RAIRO-Oper. Res. 54 (2020) 961–979. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- A. Sengupta and T.K. Pal, Fuzzy Preference Ordering of Interval Numbers in Decision Problems. Springer. Vol. 238 (2009). [CrossRef] [Google Scholar]
- A. Sengupta, T.K. Pal and D. Chakraborty, Interpretation of inequality constraints involving interval coefficients and a solution to interval linear programming. Fuzzy Sets Syst. 119 (2001) 129–138. [CrossRef] [Google Scholar]
- A.K. Sharma, S. Tiwari, V. Yadavalli and C.K. Jaggi, Optimal trade credit and replenishment policies for non-instantaneous deteriorating items. RAIRO-Oper. Res. 54 (2020) 1793–1826. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- E. Shekarian, N. Kazemi, S.H. Abdul-Rashid and E.U. Olugu, Fuzzy inventory models: a comprehensive review. Appl. Soft Comput. 55 (2017) 588–621. [CrossRef] [Google Scholar]
- K. Skouri, I. Konstantaras, S. Papachristos and I. Ganas, Inventory models with ramp type demand rate, partial backlogging and weibull deterioration rate. Eur. J. Oper. Res. 192 (2009) 79–92. [CrossRef] [Google Scholar]
- A.H. Tai, Y. Xie, W. He and W.-K. Ching, Joint inspection and inventory control for deteriorating items with random maximum lifetime. Int. J. Prod. Econ. 207 (2019) 144–162. [CrossRef] [Google Scholar]
- A.A. Taleizadeh, S.T. Niaki and A. Makui, Multiproduct multiple-buyer single-vendor supply chain problem with stochastic demand, variable lead-time, and multi-chance constraint. Expert Syst. App. 39 (2012) 5338–5348. [CrossRef] [Google Scholar]
- A.D. Torshizi and M.H.F. Zarandi, Hierarchical collapsing method for direct defuzzification of general type-2 fuzzy sets. Inf. Sci. 277 (2014) 842–861. [CrossRef] [Google Scholar]
- A.D. Torshizi, M.H.F. Zarandi and H. Zakeri, On type-reduction of type-2 fuzzy sets: a review. Appl. Soft Comput. 27 (2015) 614–627. [CrossRef] [Google Scholar]
- G.A. Widyadana and H.M. Wee, An economic production quantity model for deteriorating items with multiple production setups and rework. Int. J. Prod. Econ. 138 (2012) 62–67. [Google Scholar]
- G.A. Widyadana, L.E. Cárdenas-Barrón and H.M. Wee, Economic order quantity model for deteriorating items with planned backorder level. Math. Comput. Model. 54 (2011) 1569–1575. [Google Scholar]
- K.-S. Wu, L.-Y. Ouyang and C.-T. Yang, An optimal replenishment policy for non-instantaneous deteriorating items with stock-dependent demand and partial backlogging. Int. J. Prod. Econ. 101 (2006) 369–384. [CrossRef] [Google Scholar]
- C. Yan, A. Banerjee and L. Yang, An integrated production–distribution model for a deteriorating inventory item. Int. J. Prod. Econ. 133 (2011) 228–232. [Google Scholar]
- P.-C. Yang and H.-M. Wee, A single-vendor and multiple-buyers production-inventory policy for a deteriorating item. Eur. J. Oper. Res. 143 (2002) 570–581. [CrossRef] [Google Scholar]
- M.-J. Yao and C.-C. Chiou, On a replenishment coordination model in an integrated supply chain with one vendor and multiple buyers. Eur. J. Oper. Res. 159 (2004) 406–419. [CrossRef] [Google Scholar]
- S.H. Yoo, D. Kim and M.-S. Park, Economic production quantity model with imperfect-quality items, two-way imperfect inspection and sales return. Int. J. Prod. Econ. 121 (2009) 255–265. [CrossRef] [Google Scholar]
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