162
Views
5
CrossRef citations to date
0
Altmetric
Research Article

Three-echelon supply chain model in an imperfect production system with inspection error, learning effect, and return policy under fuzzy environment

ORCID Icon, ORCID Icon, &
Article: 1962427 | Received 13 Dec 2020, Accepted 26 Jul 2021, Published online: 15 Aug 2021

References

  • Bera, U. K., Mahapatra, N. K., & Maiti, M. (2009). An imperfect fuzzy production-inventory model over a finite time horizon under the effect of learning. International Journal of Mathematics in Operational Research, 1(3), 351–371. https://doi.org/10.1504/IJMOR.2009.024290
  • Das, P., De, S. K., & Sana, S. S. (2014). An EOQ model for time dependent backlogging over idle time: A step order fuzzy approach. International Journal of Applied and Computational Mathematics, 1(2), 1–17.
  • De, S. K. (2013). EOQ model with natural idle time and wrongly measured demand rate. International Journal of Inventory Control and Management, 3, 329–354.
  • De, S. K. (2017). Triangular dense fuzzy lock set. Soft Computing, 22, 7243–7254. https://doi.org/10.1007/s00500-017-2726-0
  • De, S. K., & Beg, I. (2016a). Triangular dense fuzzy sets and new defuzzication methods. Journal of Intelligent and Fuzzy System, 31(1), 469–477. https://doi.org/10.3233/IFS-162160
  • De, S. K., & Beg, I. (2016b). Triangular dense fuzzy Neutrosophic sets. Neutrosophic Sets and Systems, 13, 24–32. https://doi.org/10.5281/zenodo.570868
  • De, S. K., Goswami, A., & Sana, S. S. (2014). An interpolating by pass to Pareto optimality in intuitionistic fuzzy technique for an EOQ model with time sensitive backlogging. Applied Mathematics and Computation, 230, 664–674. https://doi.org/10.1016/j.amc.2013.12.137
  • De, S. K., & Mahata, G. C. (2019a). A cloudy fuzzy economic order quantity model for imperfect-quality items with allowable proportionate discounts. Journal of Industrial Engineering International, 15(4), 571–583. https://doi.org/10.1007/s40092-019-0310-1
  • De, S. K., & Mahata, G. C. (2019b). A comprehensive study of an economic order quantity model under fuzzy monsoon demand. Sadhana, 44(89), 1–12. https://doi.org/10.1007/s12046-019-1059-3
  • De, S. K., & Sana, S. S. (2013a). Backlogging EOQ model for promotional effort and selling price sensitive demand – An intuitionistic fuzzy approach. Annals of Operations Research, 233(1), 57–76. https://doi.org/10.1007/s10479-013-1476-3
  • De, S. K., & Sana, S. S. (2013b). Fuzzy order quantity inventory model with fuzzy shortage quantity and fuzzy promotional index. Economic Modelling, 31, 351–358. https://doi.org/10.1016/j.econmod.2012.11.046
  • De, S. K., & Sana, S. S. (2014). An alternative fuzzy EOQ model with backlogging for selling price and promotional effort sensitive demand. International Journal of Applied and Computational Mathematics, 1, 69–86. https://doi.org/10.1007/s40819-014-0010-x
  • De, S. K., & Sana, S. S. (2016). The (p,q,r,l) model for stochastic demand under intuitionistic fuzzy aggregation with Bonferroni mean. Journal of Intelligent Manufacturing, 29(8), 1753–1771. https://doi.org/10.1007/s10845-016-1213-2
  • Heydari, J., & Norouzinasab, Y. (2015). A two-level discount model for coordinating a decentralized supply chain considering stochastic price-sensitive demand. Journal of Industrial Engineering International, 11(4), 531–542. https://doi.org/10.1007/s40092-015-0119-5
  • Jauhari, W. A., Sulistyanto, R., & Laksono, P. W. (2018). Coordinating a two-level supply chain with defective items, inspection errors and price-sensitive demand. Songklanakarin Journal of Science and Technology, 40(1), 135–145.
  • Jauhari, W. A., Widianto, I. P., & Rosyidi, C. N. (2017). A supply chain inventory model for vendor-buyer system with defective items and imperfect inspection process. International Journal of Mathematics in Operational Research, 11(4), 450–469. https://doi.org/10.1504/IJMOR.2017.087740
  • Karmakar, S., De, S. K., & Goswami, A. (2017). A pollution sensitive dense fuzzy economic production quantity model with cycle time dependent production rate. Journal of Cleaner Production, 154, 139–150. https://doi.org/10.1016/j.jclepro.2017.03.080
  • Karmakar, S., De, S. K., & Goswami, A. (2018). A pollution sensitive remanufacturing model with waste items: Triangular dense fuzzy lock set approach. Journal of Cleaner Production, 187, 789–803. https://doi.org/10.1016/j.jclepro.2018.03.161
  • Kazemi, N., Shekarian, E., Cárdenas-Barrón, L. E., & Olugu, E. U. (2015). Incorporating human learning into a fuzzy EOQ inventory model with backorders. Computers & Industrial Engineering, 87, 540–542. https://doi.org/10.1016/j.cie.2015.05.014
  • Khan, M., Jaber, M. Y., & Ahmad, A. R. (2014). An integrated supply chain model with errors in quality inspection and learning in production. Omega, 42(1), 16–24. https://doi.org/10.1016/j.omega.2013.02.002
  • Maihami, R., Govindan, K., & Fattahi, M. (2019). The inventory and pricing decisions in a three-echelon supply chain of deteriorating items under probabilistic environment. Transportation Research Part E: Logistics and Transportation Review, 131, 118–138. https://doi.org/10.1016/j.tre.2019.07.005
  • Maity, A. K., Maity, K., Mondal, S. K., & Maiti, M. (2009). A production-recycling-inventory model with learning effect. Optimization and Engineering, 10(3), 427–438. https://doi.org/10.1007/s11081-009-9084-4
  • Maity, S., Chakraborty, A., De, S. K., Mondal, S. P., & Alam, S. (2018a). A comprehensive study of a backlogging EOQ model with nonlinear heptagonal dense fuzzy environment. RAIRO Operations Research, 54(1), 267–286. https://doi.org/10.1051/ro/2018114
  • Maity, S., De, S. K., & Mondal, S. P. (2019a). A study of an EOQ model under lock fuzzy environment. Mathematics, 7(1), 1–23. https://doi.org/10.3390/math7010075
  • Maity, S., De, S. K., & Mondal, S. P. (2019b). A study of a backorder EOQ model for cloud-type intuitionistic dense fuzzy demand rate. International Journal of Fuzzy System, 22(1), 201–211. https://doi.org/10.1007/s40815-019-00756-1
  • Maity, S., De, S. K., & Pal, M. (2018b). Two decision makers’ single decision over a back order EOQ model with dense fuzzy demand rate. Finance and Market, 3(1), 1–11. https://doi.org/10.18686/fm.v3i1.1061
  • Mohammadi, B., Taleizadeh, A. A., Noorossana, R., & Samimi, H. (2015). Optimizing integrated manufacturing and products inspection policy for deteriorating manufacturing system with imperfect inspection. Journal of Manufacturing Systems, 37, 299–315. https://doi.org/10.1016/j.jmsy.2014.08.002
  • Noori-daryan, M., & Taleizadeh, A. A. (2018). Optimizing pricing and ordering strategies in a three-level supply chain under return policy. Journal of Industrial Engineering International, 15(1), 73–80. https://doi.org/10.1007/s40092-018-0262-x
  • Parvini, M., Atashi, A., Husseini, S. M. M., & Esfahanipour, A. (2014). A two-echelon inventory model with product returns considering demands dependent return rates. International Journal of Advanced Manufacturing Technology, 72(1-4), 107–118. https://doi.org/10.1007/s00170-014-5645-6
  • Priyan, S., & Manivannan, P. (2017). Optimal inventory modeling of supply chain system involving quality inspection errors and fuzzy defective rate. OPSEARCH, 54(1), 21–43. https://doi.org/10.1007/s12597-016-0267-4
  • Salameh, M. K., Abdul-Malak, M. A. U., & Jaber, M. Y. (1993 ). Mathematical modelling of the effect of human learning in the finite production inventory model. Applied Mathematical Modelling, 17(11), 613–615. https://doi.org/10.1016/0307-904X(93)90070-W
  • Singh, S. R., & Gupta, V. (2016). Vendor – Buyer model with error in quality inspection and selling price dependent demand rate under the effect of volume agility. International Journal of Operations and Quantitative Management, 22(4), 357–371.
  • Taleizadeh, A. A., & Noori-Daryan, M. (2016). Pricing, manufacturing and inventory policies for raw material in a three-level supply chain. International Journal of Systems Science, 47(4), 919–931. https://doi.org/10.1080/00207721.2014.909544
  • Taleizadeh, A. A., Noori-daryan, M., & Tavakkoli-Moghaddam, R. (2015). Pricing and ordering decisions in a supply chain with imperfect quality items and inspection under buyback of defective items. International Journal of Production Research, 53(15), 4553–4582. https://doi.org/10.1080/00207543.2014.997399
  • Yadav, D., Singh, S. R., & Kumari, R. (2013). Inventory model with learning effect and imprecise market demand under screening error. OPSEARCH, 50(3), 418–432. https://doi.org/10.1007/s12597-012-0118-x
  • Yadav, S., Agrawal, A. K., & Vora, M. K. (2020). A single manufacturer multiple buyers integrated production-inventory model with third-party logistics. International Journal of Business Performance and Supply Chain Modelling, 11(2), 91–127. https://doi.org/10.1504/IJBPSCM.2020.109198
  • Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143–161. https://doi.org/10.1016/0020-0255(81)90017-7
  • Yoo, S. H., Kim, D., & Park, M. S. (2009). Economic production quantity model with imperfect-quality items, two-way imperfect inspection and sales return. International Journal of Production Economics, 121(1), 255–265. https://doi.org/10.1016/j.ijpe.2009.05.008
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–356. https://doi.org/10.1016/S0019-9958(65)90241-X
  • Zhao, Q., & Yang, J. (2007). Optimal pricing and return policy for enterprise under supply chain management. International Journal of Networking & Virtual Organisations, 4(2), 218–233.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.