Skip to main content

Advertisement

SpringerLink
Log in

Application of the Artificial Neural Network with Multithreading Within an Inventory Model Under Uncertainty and Inflation

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

The solutions to real-life problems are challenging to find out in the exact form as the dimensions of the problems are significant. A multi-period multi-product inventory model is tested in this study through an artificial neural network for experiencing an uncertain environment. Whenever obstacles present in every period, a neural network with multithreading is one of the optimization procedures to find the best optimal solution with an inflation and time value of money. A fuzzy approach is used here to deal with the uncertainty, and the total cost of the system is calculated using the bi-objective constraint objective function. The first objective is to find the minimum cost of the system with the optimum space, which is the second objective. The solutions of the mathematical model are obtained by generating multiple threads that every thread is a possible solution. The numerical experiment chooses the best fit from the multiple solutions. An illustrative comparative study with the existing methodologies is provided. Results show that the proposed approach is the best for cost optimization and time minimization. It is found that the neural network with multithreading converges over other algorithms and the results obtain within less than 30% time of the existing algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Kumar, R.K., Chandrawat, R.K., Sarkar, B., Joshi, V., Majumder, A.: An advanced optimization technique for smart production using α-cut based quadrilateral fuzzy number. Int. J. Fuzzy. Syst. 23, 107–127 (2021). https://doi.org/10.1007/s40815-020-01002-9

    Article  Google Scholar 

  2. Bhuniya, S., Pareek, S., Sarkar, B.: A supply chain model with service level constraints and strategies under uncertainty. Alex. Eng. J. 60(6), 6035–6052 (2021). https://doi.org/10.1016/j.aej.2021.03.039

    Article  Google Scholar 

  3. Chen, X., Simcha-Levi, D.: Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: the infinite horizon case. Math. Oper. Res. 29(3), 698–723 (2004). https://doi.org/10.1287/moor.1040.0093

    Article  MathSciNet  MATH  Google Scholar 

  4. Chen, Y.F., Ray, S., Song, Y.: Optimal pricing and inventory control policy in periodic-review systems with fixed ordering cost and lost sales. Nav. Res. Loges. 53(2), 117–136 (2006). https://doi.org/10.1002/nav.20127

    Article  MathSciNet  MATH  Google Scholar 

  5. Lee, W.C., Wu, J.W., Lei, C.L.: Computational algorithmic procedure for optimal inventory policy involving ordering cost reduction and backorder discounts when lead time demand is controllable. App. Math. Comput. 189(1), 186–200 (2007). https://doi.org/10.1016/j.amc.2006.11.057

    Article  MATH  Google Scholar 

  6. Chen, Y., Yang, S.: Time-varying effect of international iron ore price on China’s inflation: a complete price chain with TVP-SVAR-SV model. Resour. Policy. 73, 102200 (2021). https://doi.org/10.1016/j.resourpol.2021.102200

    Article  Google Scholar 

  7. Ahmed, S., Akmal, U., Shapiro, A.: Coherent risk measures in inventory problems. Eur. J. Oper. Res. 182(1), 226–238 (2007). https://doi.org/10.1016/j.ejor.2006.07.016

    Article  MathSciNet  MATH  Google Scholar 

  8. Zhang, Y., Song, S., Zhang, H., Wu, C., Yin, W.: A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand. Neur. Comput. Appl. 21(6), 1087–1098 (2012). https://doi.org/10.1007/s00521-011-0658-7

    Article  Google Scholar 

  9. Taleizadeh, A.A., Niaki, S.T.A., Aryanezhad, M.B., Tafti, A.F.: A genetic algorithm to optimize multi-product multiconstraint inventory control systems with stochastic replenishment intervals and discount. Int. J. Adv. Manuf. Tech. 51(1), 311–323 (2010). https://doi.org/10.1007/s00170-010-2604-8

    Article  Google Scholar 

  10. Das, S., Khan, M.A.A., Mahmoud, E.E., Abdel-Haleem, A., Abualnaja, K.M., Shaikh, A.A.: A production inventory model with partial trade credit policy and reliability. Alex. Eng. J. 60(1), 1325–1338 (2021). https://doi.org/10.1016/j.aej.2020.10.054

    Article  Google Scholar 

  11. Tayyab, M., Sarkar, B.: An interactive fuzzy programming approach for a sustainable supplier selection under textile supply chain management. Comp. Indust. Eng. 155, 107164 (2021). https://doi.org/10.1016/j.cie.2021.107164

    Article  Google Scholar 

  12. Dey, B.K., Bhuniya, S., Sarkar, B.: Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management. Exp. Syst. App. 184, 115464 (2021). https://doi.org/10.1016/j.eswa.2021.115464

    Article  Google Scholar 

  13. Baughman, G., Rabinovich, S.: Capacity choice, monetary trade, and the cost of inflation. Eur. Econ. Rev. 134, 103698 (2021). https://doi.org/10.1016/j.euroecorev.2021.103698

    Article  Google Scholar 

  14. Mahapatra, N.K., Maiti, M.: Decision process for multi-objective, multi-item production-inventory system via interactive fuzzy satisficing technique. Comp. Math. App. 49(5), 805–821 (2005). https://doi.org/10.1016/j.camwa.2004.07.020

    Article  MATH  Google Scholar 

  15. Maity, K., Maiti, M.: A numerical approach to a multi-objective optimal inventory control problem for deteriorating multi-items under fuzzy inflation and discounting. Comp. Math. App. 55(8), 1794–1807 (2008). https://doi.org/10.1016/j.camwa.2007.07.011

    Article  MathSciNet  MATH  Google Scholar 

  16. Dey, J.K., Mondal, S.K., Maiti, M.: Two storage inventory problem with dynamic demand and interval valued lead-time over finite time horizon under inflation and time-value of money. Eur. J. Oper. Res. 185(1), 170–194 (2008). https://doi.org/10.1016/j.ejor.2006.12.037

    Article  MathSciNet  MATH  Google Scholar 

  17. Edalatpanah, S.A.: Neutrosophic structured element. Exp. Syst. 37(5), e12542 (2020). https://doi.org/10.1111/exsy.12542

    Article  Google Scholar 

  18. Mirzazadeh, A., Ghomi, S.F.: Esfahani, MS: A multiple items inventory model under uncertain external inflationary conditions. Trend. App. Sci. Res. 6(5), 472 (2011)

  19. Yadav, D., Kumari, R., Kumar, N., Sarkar, B.: Reduction of waste and carbon emission through the selection of items with cross-price elasticity of demand to form a sustainable supply chain with preservation technology. J. Clean. Prod. 297, 126298 (2021). https://doi.org/10.1016/j.jclepro.2021.126298

    Article  Google Scholar 

  20. Ullah, M., Asghar, I., Zahid, M., Omair, M., Alarjani, A., Sarkar, B.: Ramification of remanufacturing in a sustainable three-echelon closed-loop supply chain management for returnable products. J. Clean. Prod. 290, 125609 (2021). https://doi.org/10.1016/j.jclepro.2020.125609

    Article  Google Scholar 

  21. Deb, K., Mitra, K., Dewri, R., Majumdar, S.: Towards a better understanding of the epoxy-polymerization process using multi-objective evolutionary computation. Chem. Eng. Sci. 59(20), 4261–4277 (2004). https://doi.org/10.1016/j.ces.2004.06.012

    Article  Google Scholar 

  22. Mousavi, S.M., Hajipour, V., Niaki, S.T.A., Alikar, N.: Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: Two calibrated meta-heuristic algorithms. App. Math. Model. 37(4), 2241–2256 (2013). https://doi.org/10.1016/j.apm.2012.05.019

    Article  MathSciNet  MATH  Google Scholar 

  23. Sadeghi, J., Niaki, S.T.A.: Two parameter tuned multi-objective evolutionary algorithms for a bi-objective vendor managed inventory model with trapezoidal fuzzy demand. App. Soft Comput. 30, 567–576 (2015). https://doi.org/10.1016/j.asoc.2015.02.013

    Article  Google Scholar 

  24. Li, C.J., Chen, G.D., Sung, T.Y., Tsai, H.F.: Novel adaptive Kalman filter with fuzzy neural network for trajectory estimation system. Int. J. Fuzzy Syst. 21, 1649–1660 (2019). https://doi.org/10.1007/s40815-019-00686-y

    Article  Google Scholar 

  25. Lin, Q., Chen, S., Lin, C.M.: An optimization method for the initial parameters selection of fuzzy cerebellar model neural networks in parametric fault diagnosis. Int. J. Fuzzy Syst. 22, 2071–2081 (2020). https://doi.org/10.1007/s40815-020-00908-8

    Article  Google Scholar 

  26. Naderkhani, R., Behzad, M.H., Razzaghnia, T., Farnoosh, R.: Fuzzy regression analysis based on fuzzy neural networks using trapezoidal data. Int. J. Fuzzy Syst. (2021). https://doi.org/10.1007/s40815-020-01033-2

    Article  Google Scholar 

  27. Fattahi, P., Hajipour, V., Nobari, A.: A bi-objective continuous review inventory control model: pareto-based meta-heuristic algorithms. App. Soft Comput. 32, 211–223 (2015). https://doi.org/10.1016/j.asoc.2015.02.044

    Article  Google Scholar 

  28. Das, S.C., Manna, A.K., Rahman, M.S., Shaikh, A.A., Bhunia, A.K.: An inventory model for non-instantaneous deteriorating items with preservation technology and multiple credit periods-based trade credit financing via particle swarm optimization. Soft Comput. 25, 5365–5384 (2021). https://doi.org/10.1007/s00500-020-05535-x

    Article  Google Scholar 

  29. Khurshid, B., Maqsood, S., Omair, M., Sarkar, B., Ahmed, I., Muhammad, K.: An improved evolution strategy hybridization with simulated annealing for permutation flow shop scheduling problems. IEEE Access. 9, 94505–94522 (2021). https://doi.org/10.1109/ACCESS.2021.3093336

    Article  Google Scholar 

  30. Saha, S., Chatterjee, D., Sarkar, B.: The ramification of dynamic investment on the promotion and preservation technology for inventory management through a modified flower pollination algorithm. J. Retail. Consum. Ser. 58, 102326 (2021). https://doi.org/10.1016/j.jretconser.2020.102326

    Article  Google Scholar 

  31. Jin, F., Liu, J., Zhou, L., Martínez, L.: Consensus-based linguistic distribution large-scale group decision making using statistical inference and regret theory. Group Dec. Negot. 30, 813–845 (2021). https://doi.org/10.1007/s10726-021-09736-z

    Article  Google Scholar 

  32. Jin, F., Garg, H., Pei, L., Liu, J., Chen, H.: Multiplicative consistency adjustment model and data envelopment analysis-driven decision-making process with probabilistic hesitant fuzzy preference relations. Int. J. Fuzzy Syst. 22(7), 2319–2332 (2020). https://doi.org/10.1007/s40815-020-00944-4

    Article  Google Scholar 

  33. Jin, F., Cao, M., Liu, J., Martínez, L., Chen, H.: Consistency and trust relationship-driven social network group decision-making method with probabilistic linguistic information. Appl. Soft Comput. 103, 107170 (2021). https://doi.org/10.1016/j.asoc.2021.107170

    Article  Google Scholar 

  34. Jin, F., Ni, Z., Langari, R., Chen, H.: Consistency improvement-driven decision-making methods with probabilistic multiplicative preference relations. Group Dec. Negot. 29, 371–397 (2020). https://doi.org/10.1007/s10726-020-09658-2

    Article  Google Scholar 

  35. Gupta, S., Haq, A., Ali, I., Sarkar, B.: Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment. Complex. Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00326-9

    Article  Google Scholar 

  36. Taleizadeh, A.A., Moghadasi, H., Niaki, S.T.A., Eftekhari, A.: An EOQ-joint replenishment policy to supply expensive imported raw materials with payment in advance. J. App. Sci. 8(23), 4263–4273 (2008)

  37. Daroudi, S., Kazemipoor, H., Najafi, E., Fallah, M.: The minimum latency in location routing fuzzy inventory problem for perishable multi-product materials. Appl. Soft. Comput. 110, 107543 (2021). https://doi.org/10.1016/j.asoc.2021.107543

    Article  Google Scholar 

  38. Zhou, Y.: A novel similarity measure for generalized trapezoidal fuzzy numbers and its application to decision-making. Int. J. Ser. Sci. Tech. 9(3), 131–148 (2016). https://doi.org/10.14257/IJUNESST.2016.9.3.14

  39. Mahapatra, A.M., Soni, H.N., Mahapatra, M.S., Sarkar, B., Majumder, S.: A continuous review production-inventory system with a variable preparation time in a fuzzy random environment. Mathematics 9(7), 747 (2021). https://doi.org/10.3390/math9070747

    Article  Google Scholar 

  40. Omair, M., Noor, S., Tayyab, M., Maqsood, S., Ahmed, W., Sarkar, B., Habib, M.S.: The selection of the sustainable suppliers by the development of a decision support framework based on analytical hierarchical process and fuzzy inference system. Int. J. Fuzzy. Syst. (2021). https://doi.org/10.1007/s40815-021-01073-2

    Article  Google Scholar 

  41. Das, S.K.: Multi item inventory model include lead time with demand dependent production cost and set-up-cost in fuzzy environment. J. Fuzzy Extend. App. 1(3), 241–258 (2020). https://doi.org/10.22105/JFEA.2020.254081.1025

    Article  Google Scholar 

  42. Samanta, S., Dubey, V.K., Sarkar, B.: Measure of influences in social networks. App. Soft. Comput. 99, 106858 (2021). https://doi.org/10.1016/j.asoc.2020.106858

    Article  Google Scholar 

  43. Sharma, S., Singh, S.R., Kumar, M.A.: Reverse logistics inventory model with multiple production and remanufacturing batches under fuzzy environment. RAIRO Oper. Res. 55(2), 571–588 (2021). https://doi.org/10.1051/ro/2021021

    Article  MathSciNet  MATH  Google Scholar 

  44. Garai, A., Chowdhury, S., Sarkar, B., Roy, T.K.: Cost-effective subsidy policy for growers and biofuels-plants in closed-loop supply chain of herbs and herbal medicines: An interactive bi-objective optimization in T-environment. Appl. Soft. Comput. 100, 106949 (2021). https://doi.org/10.1016/j.asoc.2020.106949

    Article  Google Scholar 

  45. Glock, C.H., Schwindl, K., Jaber, M.Y.: An EOQ model with fuzzy demand and learning in fuzziness. Int. J. Serv. Oper. Manag. 12(1), 90–100 (2012). https://doi.org/10.1504/IJSOM.2012.046675

    Article  Google Scholar 

  46. Sadeghi, J., Niaki, S.T.A., Malekian, M.R., Sadeghi, S.: Optimising multi-item economic production quantity model with trapezoidal fuzzy demand and backordering: two tuned meta-heuristics. Euro. J. Indust. Eng. 10(2), 170–195 (2016). https://doi.org/10.1504/EJIE.2016.075847

    Article  Google Scholar 

  47. Hsu, M.J., Chien, Y.H., Wang, W.Y., Hsu, C.C.: A convolutional fuzzy neural network architecture for object classification with small training database. Int. J. Fuzzy Syst. 22, 1–10 (2020). https://doi.org/10.1007/s40815-019-00764-1

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported (in part) by the Yonsei University Research Fund (Post Doc. Researcher Supporting Program) of 2020 (project no.: 2020-12-0136). The work is supported by the National Research Foundation of Korea (NRF) grant, funded by the Korea Government (MSIT) (NRF-2020R1F1A1064460).

Author information

Authors and Affiliations

  1. Department of Industrial & Management Engineering, Hanyang University, Ansan, 15588, Gyeonggi-do, South Korea

    Arijit Sarkar

  2. Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul, 03722, South Korea

    Rekha Guchhait & Biswajit Sarkar

  3. Center for Transdisciplinary Research (CFTR), Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, 162, Poonamallee High Road, Velappanchavadi, Chennai, Tamil Nadu, 600077, India

    Biswajit Sarkar

Authors
  1. Arijit Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rekha Guchhait
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Biswajit Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Biswajit Sarkar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sarkar, A., Guchhait, R. & Sarkar, B. Application of the Artificial Neural Network with Multithreading Within an Inventory Model Under Uncertainty and Inflation. Int. J. Fuzzy Syst. 24, 2318–2332 (2022). https://doi.org/10.1007/s40815-022-01276-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-022-01276-1

Keywords

Search

Navigation