Catalyzing resilience: Multi-faceted optimization of single vendor-multi buyer supply chains amidst stochastic demand

Document Type : Research Paper

Author

Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

In the contemporary supply chain management landscape, the intricacies of managing a single vendor-multi-buyer network amidst stochastic demand pose significant challenges. This paper delves into optimizing such supply chains, emphasizing resilience in the face of uncertain demand scenarios. Leveraging the NSGA-II (Non-dominated Sorting Genetic Algorithm II), a powerful evolutionary optimization technique, we explore the multifaceted dimensions of supply chain optimization. The proposed framework aims to enhance the robustness and adaptability of supply chain networks by simultaneously addressing two key objectives: minimizing costs and maximizing service levels. By considering stochastic demand patterns, inherent uncertainties are meticulously accounted for, ensuring that the optimized solutions are efficient and resilient to unforeseen fluctuations in demand. This study comprehensively evaluates the single vendor-multi buyer supply chain model and highlights the efficacy of the NSGA-II algorithm in navigating the complex trade-offs inherent in supply chain optimization. By generating diverse Pareto-optimal solutions, the algorithm empowers decision-makers with actionable insights, enabling them to make informed choices that balance cost-effectiveness with service quality. Furthermore, this paper contributes to the evolving discourse on supply chain resilience by integrating advanced optimization methodologies with real-world supply chain dynamics. The findings underscore the importance of proactive optimization strategies in building resilient supply chain networks capable of withstanding the volatility of today's global marketplace. In conclusion, this research illuminates the path towards catalyzing resilience in single vendor-multi buyer supply chains, offering a nuanced understanding of the interplay between optimization algorithms, stochastic demand, and supply chain performance. Organizations can fortify their supply chain architectures through continuous refinement and adaptation, fostering agility and competitiveness in an ever-evolving business landscape.

Keywords

[1] T.F. Abdelmaguid and M.M. Dessouky, A genetic algorithm approach to the integrated inventory-distribution problem, Int. J. Prod. Res. 44 (2006), no. 21, 4445–4464.
[2] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, Parallel Problem Solving from Nature PPSN VI: 6th Int. Conf. Paris, France, Proc., Springer Berlin Heidelberg, Vol. 6, 2000, pp. 849–858.
[3] S.K. Goyal, An integrated inventory model for a single supplier-single customer problem, Int. J. Prod. Res. 15 (1976), no. 1, 107–111.
[4] S.K. Goyal, A joint economic-lot-size model for purchaser and vendor: A comment, Decision Sci. 19 (1988), no. 1, 236–241.
[5] F. Hosseinzadeh Lotfi, T. Allahviranloo, M. Shafiee, and H. Saleh, Supply chain management, Supply chain performance evaluation, Studies in Big Data, Springer, Cham, 2023.
[6] J.K. Jha and K. Shanker, An integrated inventory problem with transportation in a divergent supply chain under service level constraint, J. Manufact. Syst. 33 (2014), no. 4, 462–475.
[7] W. Lee, A joint economic lot size model for raw material ordering, manufacturing setup, and finished goods delivery, Omega-Int. J. Manag. Sci. 33 (2005), no. 2, 163–174.
[8] Y.J. Lin, An integrated vendor–buyer inventory model with backorder price discount and effective investment to reduce ordering cost, Comput. Ind. Eng. 56 (2009), no. 4, 1597–1606.
[9] L. Lu, A one-vendor multi-buyer integrated inventory model, Eur. J. Oper. Res. 81 (1995), no. 2, 312–323.
[10] H. Mahmoudi, M. Sharifi, M.R. Shahriari, and M.A. Shafiee, Solving a reverse logistic model for multilevel supply chain using genetic algorithm, Int. J. Ind. Math. 12 (2020), no. 2, 177–188.
[11] V. Mohagheghi, S.M. Mousavi, B. Vahdani, and M.R. Shahriari, R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach, Neural Comput. Appl. 28 (2017), no. 12, 3869–3888.
[12] S.P. Nachiappan, A. Gunasekaran, and N. Jawahar, Knowledge management system for operating parameters in two-echelon VMI supply chains, Int. J. Prod. Res. 45 (2007), no. 11, 2479–2505.
[13] M.A. Nayebi, M. Sharifi, M.R. Shahriari, and O. Zarabadipour, Fuzzy-chance constrained multi-objective programming applications for inventory control model, Appl. Math. Sci. 6 (2012), no. 5, 209–228.
[14] S. Nourali, N. Imanipour, and M.R. Shahriari, A mathematical model for integrated process planning and scheduling in flexible assembly job shop environment with sequence-dependent setup times, Int. J. Math. Anal. 6 (2012), no. 41–44, 2117–2132.
[15] C.H.J. Pan and J.S. Yang, A study of an integrated inventory with controllable lead time, Int. J. Prod. Res. 40 (2002), no. 5, 1263–1273.
[16] H. Rahimi Sheikh, M. Sharifi, and M.R. Shahriari, Designing a resiliense supply chain model (case study: the welfare organization of Iran), J. Ind. Manag. Persp. 7 (2017), no. 3, 127–150.
[17] H.I.L.D.A. Saleh, F. Hosseinzadeh Lotfi, M. Rostmay-Malkhalifeh, and M. Shafiee, Provide a mathematical model for selecting suppliers in the supply chain based on profit efficiency calculations, J. New Res. Math. 7 (2021), no. 32, 177–186. 
[18] M.R. Shahriari, A cultural algorithm for data clustering, Int. J. Ind. Math. 8 (2016), no. 2.
[19] M.R. Shahriari, Set a bi-objective redundancy allocation model to optimize the reliability and cost of the Series parallel systems using NSGA II problem, Int. J. Ind. Math. 8 (2016), no. 3, 171–176.
[20] M. Shahriari, Multi-objective optimization of discrete time–cost tradeoff problem in project networks using nondominated sorting genetic algorithm, J. Ind. Eng. Int. 12 (2016), no. 2, 159–169.
[21] M.R. Shahriari, Soft computing based on a modified MCDM approach under intuitionistic fuzzy sets, Iran. J. Fuzzy Syst. 14 (2017), no. 1, 23–41.
[22] M.R. Shahriari, Using genetic algorithm to optimize a system with repairable components and multi-vacations for repairmen, Int. J. Nonlinear Anal. Appl. 13 (2022), no. 2, 3139–3144.
[23] M.R. Shahriari, Redundancy allocation optimization based on the fuzzy universal generating function approach in the series-parallel systems, Int. J. Ind. Math. 15 (2023), no. 1, 69–77.
[24] M.R. Shahriari and N. Pilevari, Agile supplier selection in sanitation supply chain using fuzzy VIKOR method, J. Optim. Ind. Eng. 21 (2017), 19–28.
[25] M. Sharifi, P. Pourkarim Guilani, and M. Shahriari, Using NSGA II algorithm for a three objectives redundancy allocation problem with k-out-of-n sub-systems, J. Optim. Ind. Eng. 9 (2016), no. 19, 87–96.
[26] M. Sharifi, M.R. Shahriari, and A. Zaretalab, The effects of technical and organizational activities on redundancy allocation problem with choice of selecting redundancy strategies using the memetic algorithm, Int. J. Ind. Math. 11 (2019), no. 3, 165–176.
[27] N. Srinivas and K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms, Evol. Comput. 2 (1994), no. 3, 221–248.
[28] B. Vahdani, S.S. Behzadi, S.M. Mousavi, and M.R. Shahriari, A dynamic virtual air hub location problem with balancing requirements via robust optimization: Mathematical modeling and solution methods, J. Intell. Fuzzy Syst. 31 (2016), no. 3, 1521-1534.
[29] A. Zaretalab, V. Hajipour, M. Sharifi, and M.R. Shahriari, A knowledge-based archive multi-objective simulated annealing algorithm to optimize series-parallel system with choice of redundancy strategies, Comput. Ind. Eng. 80 (2015), 33–44.
Volume 16, Issue 3
March 2025
Pages 77-88
  • Receive Date: 09 January 2024
  • Revise Date: 07 May 2024
  • Accept Date: 14 May 2024