Presenting a mathematical model for supply chain network design considering trade credit under uncertainty

Document Type : Original Article

Authors

1 PhD student, Department of Industrial Management and Information Technology, Faculty of Mirit and Accounting, Shahid Beheshti University, Tehran, Iran

2 Assistant Professor, Department of Industrial Management and Information Technology, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

3 Associate Professor, Department of Industrial Management and Information Technology, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

Abstract

Providing financial resources is necessary for the survival of any business. In supply chain networks, bank loans and commercial credits play a crucial role in financing. Supply chain networks are always affected by financial disturbances under uncertainty condition, therefore, the design of supply chain networks considering financial flows leads to the improvement of working capital. In this research, the supply chain network is designed and developed considering commercial credibility. Considering commercial credit at all levels in a three-level supply chain network including suppliers, factories and distribution centers can be stated as the main contribution of this study. In addition, considering the timing for the repayment of commercial credits by the factories and distribution centers in uncertainty conditions is another challenge of the present research. Due to the uncertainty of demand, supply chain planning should be done in such a way that the necessary financial resources for the production operations are incorporated. In this regard, the demand is considered under scenario-based uncertainty in the proposed model in which the maximization of the net present value as well as the demand estimate are the main objectives. The CPLEX solver was used for solving the model in small-sized instances and the Bee Colony and Wale multi-objective metaheuristic algorithms were used for solving the large-sized problems. The results show how commercial credit affects physical flow. Also, the Wale metaheuristic algorithm has a better performance than the other algorithm.

Keywords

References

[1]     Dorri Nokurani B, Zandiyeh M, and Notash M, “Multi-objective design of supply     chain network with genetic algorithm approach,” Management Research in Iran,     vol.18, no. 4, pp.183-203, 2021,[in persian],dor: 20.1001.1.2322200.1393.18.4.9.6.
[2]     Arani H. V, and Torabi S. A, “Integrated material-financial supply chain master planning under mixed uncertainty,” Inf. Sci. (Ny)., vol. 423, pp. 96–114, 2018, doi: 10.1016/j.ins.2017.09.045.
[3]    Alavi S. H, and Jabbarzadeh A, “Supply chain network design using trade credit and bank credit: A robust optimization model with real world application,” Comput. Ind. Eng., vol. 125, no. August, pp. 69–86, 2018, doi: 10.1016/j.cie.2018.08.005.
[4]    Ramezani M, Kimiagari A. M, and Karimi B, “Closed-loop supply chain network design: A financial approach,” Appl. Math. Model., vol. 38, no. 15–16, pp. 4099–4119, 2014, doi: 10.1016/j.apm.2014.02.004.
[5]    Chen X, and Wang A, “Trade credit contract with limited liability in the supply chain with budget constraints,” Ann. Oper. Res., vol. 196, no. 1, pp. 153–165, 2012, doi: 10.1007/s10479-012-1119-0.
[6]    Brahmi A, Hadj-Alouane A. B, and Sboui S, “Dynamic and reactive optimization of physical and financial flows in the supply chain,” Int. J. Ind. Eng. Comput., vol. 11, no. 1, pp. 83–106, 2020, doi: 10.5267/j.ijiec.2019.6.003.
[7]    Sadeghi Moghadam M. R, Karimi T, and Bandesi S, “Evaluating the risks of service  supply chain with the rough set theory approach (case study: companies providing                       payment services to banks),” Management Research in Iran, vol. 22, no. 1, pp.69-94,      2021,[in persian],dor: 20.1001.1.2322200.1397.22.1.4.3.
 [8]    Mohammadi A, Khalife M, Ali Mohammadlou A, Abbasi A, and Eghtesadifard M,    “Operational and financial design of multi-level supply chain system in strategic and  tactical decision-making levels,” Modern researches in decision making, vol. 3, no. 1,  pp. 267-297, 2018,[in persian], doi: https://journal.saim.ir/article_31261.html. 
[9]    Khakbazan E, Chaharsoghi S. K, and Rafiei F, “Providing a value-based integrated supply chain model by considering financial ratios in financial   decisions,” Modern                   researches in decision making, vol. 3, no. 31, pp. 113-  136, 2018, [in persian],doi: https://journal.saim.ir/article_31248.html.       
[10]    Nickel S, Saldanha-da-Gama S, and Ziegler H. P, “A multi-stage stochastic supply network design problem with financial decisions and risk management,” Omega, vol. 40, no. 5, pp. 511–524, 2012, doi: 10.1016/j.omega.2011.09.006.
[11]    Cardoso S. R, Barbosa-Póvoa A. P. F. D., and Relvas S, “Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty,” Eur. J. Oper. Res., vol. 226, no. 3, pp. 436–451, 2013, doi: 10.1016/j.ejor.2012.11.035.
[12]    Mohammadi A, Abbasi A, Alimohammadlou M, Eghtesadifard M, and Khalifeh M, “Optimal design of a multi-echelon supply chain in a system thinking framework: An integrated financial-operational approach,” Comput. Ind. Eng., vol. 114, pp. 297–315, 2017, doi: 10.1016/j.cie.2017.10.019.
[13]    Zhong Y, Shu J, Xie W, and Zhou Y. W, “Optimal trade credit and replenishment policies for supply chain network design,” Omega (United Kingdom), vol. 81, pp. 26–37, 2018, doi: 10.1016/j.omega.2017.09.006.
[14]    Nobil A. H, Jalali S, and Niaki S. T. A., “Financially embedded facility location decisions on designing a supply chain structure: A case study,” Syst. Eng., vol. 21, no. 6, pp. 520–533, 2018, doi: 10.1002/sys.21452.
[15]    Wang M, and Huang H, “The design of a flexible capital-constrained global supply chain by integrating operational and financial strategies,” Omega (United Kingdom), vol. 88, pp. 40–62, 2019, doi: 10.1016/j.omega.2018.11.016.
[16]    Polo A, Peña N, Muñoz D, Cañón A, and Escobar J. W., “Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria,” Omega (United Kingdom), vol. 88, pp. 110–132, 2019, doi: 10.1016/j.omega.2018.09.003.
[17]    Lu Q, Gu J, and Huang J, “Supply chain finance with partial credit guarantee provided by a third-party or a supplier,” Comput. Ind. Eng., vol. 135, no. October 2018, pp. 440–455, 2019, doi: 10.1016/j.cie.2019.06.026.
[18]    Darestani S. A, and Pourasadollah F, “A multi-objective fuzzy approach to closed-loop supply chain network design with regard to dynamic pricing,” J. Optim. Ind. Eng., vol. 12, no. 1, pp. 173–194, 2019, doi: 10.22094/joie.2018.476.0.
[19]    Rahimi M, Ghezavati V, and Asadi F, “A stochastic risk-averse sustainable supply chain network design problem with quantity discount considering multiple sources of uncertainty,” Comput. Ind. Eng., vol. 130, no. June 2018, pp. 430–449, 2019, doi: 10.1016/j.cie.2019.02.037.
[20]    Goli A, Zare H. K, Tavakkoli-Moghaddam R, and Sadegheih A, “Multiobjective fuzzy mathematical model for a financially constrained closed-loop supply chain with labor employment,” Comput. Intell., vol. 36, no. 1, pp. 4–34, 2020, doi: 10.1111/coin.12228.
[21]    Huang J, Yang W, and Tu Y, “Financing mode decision in a supply chain with financial constraint,” Int. J. Prod. Econ., vol. 220, 2020, doi: 10.1016/j.ijpe.2019.07.014.
[22]    Tsao Y. C, Zhang Q, Zhang X, and Vu T. L, “Supply chain network design for perishable products under trade credit,” J. Ind. Prod. Eng., vol. 38, no. 6, pp. 466–474, 2021, doi: 10.1080/21681015.2021.1937722.
[23]    Ding Y, Jiang Y, Wu L, and Zhou Z, “Two-echelon supply chain network design with trade credit,” Comput. Oper. Res., vol. 131, no. January, p. 105270, 2021, doi: 10.1016/j.cor.2021.105270.
[24]    Mirjalili S, and Lewis A, “The Whale Optimization Algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016, doi: 10.1016/j.advengsoft.2016.01.008.
[25]    Mastrocinque E, Yuce B, Lambiase A, and Packianather M. S, “A multi-objective optimization for supply chain network using the bees algorithm,” Int. J. Eng. Bus. Manag., vol. 5, no. 1, pp. 1–11, 2013, doi: 10.5772/56754.
[26]    Behnamian J, Ghomi S. M. T. F, and Zandieh M, “Expert Systems with Applications A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic,” Expert Syst. Appl., vol. 36, no. 8, pp. 11057–11069, 2009, doi: 10.1016/j.eswa.2009.02.080.
[27]    Heidari A, Mohammad D, and Mohammad I, Green two ‑ echelon closed and open location ‑ routing problem : application of NSGA ‑ II and MOGWO metaheuristic approaches, no. 0123456789. Springer Netherlands, 2022. doi: 10.1007/s10668-022-02429-w.
[28]    Heidari A, Imani D. M., and Khalilzadeh M, “A hub location model in the sustainable supply chain considering customer segmentation,” J. Eng. Des. Technol., vol. 19, no. 6, pp. 1387–1420, 2021, doi:

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