A Stochastic Mathematical Programming Approach to Resilient Supplier Selection and Order Allocation Problem: A Case Study in Iran Khodro Supply Chain

Document Type : Article

Authors

1 Management and Accounting Faculty, Allameh Tabatabaie University, Tehran, Iran

2 Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran

3 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

10.24200/sci.2021.56020.4515

Abstract

Suppliers as one of the main sources of vulnerability may lead to disruption and risk in supply chains. Thus, resilient supplier selection can lead to an increase in the resilience of the supply process, especially in automotive supply chains. The goal of this study is to select a set of resilient suppliers and optimal demand allocation in an automotive supply chain under risk. For this purpose, a bi-objective two-stage stochastic programming model is presented. In contrast to previous mathematical models, our model includes a new objective function to consider the supplier’s delivery performance as one of the criteria of resilient supplier selection and also the k-means clustering method is used to cluster and decrease the number of disruption scenarios. In the proposed model, due to the uncertainty of demand, chance-constrained programming approach has been utilized. The augmented Ɛ-constraint method is implemented to solve the presented model. Finally, sensitivity analysis has been done to determine the effect of parameter changes on the final results. The results of the research indicate that contingency planning can reduce the effect of disruption risks. The findings also show that the strategy of the supply chain regionalization is important in reducing the effects of environmental disruption.

Keywords

References

  1. References

    1. Mendoza, A. and Ventura, J.A. “Analytical models for supplier selection and order quantity allocation”, Applied Mathematical Modelling, 36(8), pp. 3826-3835 (2012).
    2. Torabi, S.A., Baghersad, M. and Mansouri, S.A. “Resilient supplier selection and order allocation under operational and disruption risks”, Transportation Research Part E: Logistics and Transportation Review, 79, pp. 22-48 (2015).
    3. Hosseini, S. and Al Khaled, A. “A hybrid ensemble and AHP approach for resilient supplier selection”, Journal of Intelligent Manufacturing, 30(1), pp. 207-228 (2019).
    4. Kamalahmadi, M. and Parast, M.M. “An assessment of supply chain disruption mitigation strategies”, International Journal of Production Economics, 184, pp. 210-230 (2017).
    5. Carvalho, H., Maleki, M. and Cruz-Machado, V. “The links between supply chain disturbances and resilience strategies”, International Journal of Agile Systems and Management, 5(3), pp. 203-234 (2012).
    6. Tukamuhabwa, B.R., Stevenson, M., Busby, J., et al. “Supply chain resilience: definition, review and theoretical foundations for further study”, International Journal of Production Research, 53(18), pp. 5592-5623 (2015).
    7. Soni, U., Jain, V. and Kumar, S. “Measuring supply chain resilience using a deterministic modeling approach”, Computers & Industrial Engineering, 74, pp. 11-25 (2014).
    8. Rice, J.B. and Caniato, F. “Building a secure and resilient supply network”, Supply Chain Management Review, 7, pp. 22-30 (2003).
    9. Christopher, M. and Peck, H. “Building the resilient supply chain”, The international journal of logistics management, 15(2), pp. 1-14 (2004).
    10. Jabbarzadeh, A., Fahimnia, B., Sheu, J.B., et al. “Designing a supply chain resilient to major disruptions and supply/demand interruptions”, Transportation Research Part B: Methodological, 94, pp. 121-149 (2016).
    11. Hou, J., Zeng, A.Z. and Zhao, L. “Coordination with a backup supplier through buy-back contract under supply disruption”, Transportation Research Part E: Logistics and Transportation Review, 46(6), pp. 881-895 (2010).
    12. Christopher, M. and Lee, H. “Mitigating supply chain risk through improved confidence”, International journal of physical distribution & logistics management, 34(5), pp. 388-396 (2004).
    13. Rajesh, R. and Ravi, V. “Supplier selection in resilient supply chains: a grey relational analysis approach”, Journal of Cleaner Production, 86, pp. 343-359 (2015).
    14. Kamalahmadi, M. and Mellat-Parast, M. “Developing a resilient supply chain through supplier flexibility and reliability assessment”, International Journal of Production Research, 54(1), pp. 302-32 (2016).
    15. Chopra, S. and Sodhi, M. “Reducing the risk of supply chain disruptions”, MIT Sloan Management Review, 55(3), pp.72-80 (2014).
    16. Berger, P.D., Gerstenfeld, A. and Zeng, A.Z. “How many suppliers are best? A decision-analysis approach”, Omega, 32(1),pp.  9-15 (2004).
    17. Sarkar, A. and Mohapatra, P.K. “Determining the optimal size of supply base with the consideration of risks of supply disruptions”, International Journal of Production Economics, 119(1), pp. 122-135 (2009).
    18. Sawik, T. “On the robust decision-making in a supply chain under disruption risks”, International Journal of Production Research, 52(22), pp. 6760-6781 (2014).
    19. Sawik, T. “Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing”, Computers & Operations Research, 51, pp. 11-20 (2014).
    20. PrasannaVenkatesan, S. and Goh, M. “Multi-objective supplier selection and order allocation under disruption risk”, Transportation Research Part E: Logistics and Transportation Review, 95, pp. 124-142 (2016).
    21. Sawik, T. “Selection of supply portfolio under disruption risks”, Omega, 39(2), pp. 194-208 (2011).
    22. Lee, S.H. “A fuzzy multi-objective programming approach for determination of resilient supply portfolio under supply failure risks”, Journal of Purchasing and Supply Management, 23(3), pp. 211-220 (2017).
    23. Sheffi, Y. “Building a resilient supply chain”, Harvard Business Review, 1, pp. 1-11 (2005).
    24. Ruiz-Torres, A.J. and Mahmoodi, F. “A supplier allocation model considering delivery failure, maintenance and supplier cycle costs”, International Journal of Production Economics, 103(2), pp. 755-766 (2006).
    25. Moritz, S. and Pibernik, R. “The optimal number of suppliers in the presence of volume discounts and different compensation potentials-an analytical and numerical analysis”, European Business School Research, (2008).
    26. Ruiz-Torres, A.J., Mahmoodi, F. and Zeng, A.Z. “Supplier selection model with contingency planning for supplier failures”, Computers & Industrial Engineering, 66(2), pp. 374-382 (2013).
    27. Sawik, T. “Selection of resilient supply portfolio under disruption risks”, Omega, 41(2), pp. 259-269 (2013).
    28. Solgi, O., Gheidar-Kheljani, J., Dehghani, E., et al. “Resilient supplier selection in complex product and its subsystems’ supply chain under uncertainty and risk disruption: A case study for satellite components”, Scientia Iranica, (2019).
    29. Sheffi, Y. “Preparing for the big one [supply chain management]”, Manufacturing Engineer, 84, pp. 12-15 (2005).
    30. Rawls, C.G. and Turnquist, M.A. “Pre-positioning of emergency supplies for disaster response”, Transportation research part B: Methodological, 44(4), pp. 521-534 (2010).
    31. Zheng, Y.J., Wang, Y., Ling, H.F., et al. “Integrated civilian–military pre-positioning of emergency supplies: A multi-objective optimization approach”, Applied Soft Computing, 58, pp. 732-741 (2017).
    32. Hosseini, S., Morshedlou, N., Ivanov, D., et al. “Resilient supplier selection and optimal order allocation under disruption risks”, International Journal of Production Economics, 213, pp. 124-137 (2019).
    33. Sahebjamnia, N. “Resilience supplier selection and order allocation under uncertainty”, Scientia Iranica (In Press)(2018). https://doi.org/10.24200/sci.2018.5547.1337
    34. Kaur, H. and Singh, S. P. “Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies”, International Journal of Production Economics, 231, pp. 107830 (2021).
    35. Olanrewaju, O. G., Dong, Z. S. and Hu, S. “Supplier selection decision making in disaster response”, Computers & Industrial Engineering, 143, pp. 106412 (2020).
    36. Falasca, M. and Zobel, C.W. “A two-stage procurement model for humanitarian relief supply chains”, Journal of Humanitarian Logistics and Supply Chain Management, 1(2), pp. 151-169 (2011).
    37. Zhalechian, M., Tavakkoli-Moghaddam, R. and Rahimi, Y. “A self-adaptive evolutionary algorithm for a fuzzy multi-objective hub location problem: An integration of responsiveness and social responsibility”, Engineering Applications of Artificial Intelligence, 62, pp. 1-16 (2017).
    38. Zhalechian, M., Torabi, S.A. and Mohammadi, M. “Hub-and-spoke network design under operational and disruption risks”, Transportation research part E: logistics and transportation review, 109, pp. 20-43 (2018).
    39. Mir Hasani, S.A. and Hooshmand Khaligh, F. “Stochastic programming”, 2th Edn. , pp. 149-153, Amirkabir University of Technology Publications, Tehran (2015).
    40. Mavrotas, G. “Effective implementation of Ɛ-constraint method in multi-objective mathematical programming problems”, Applied Mathematics and Computation, 213, pp. 455-465 (2009).
    41. Felfel, H., Ayadi, O. and Masmoudi, F. “A decision-making approach for a multi-objective multisite supply network planning problem”, International Journal of Computer Integrated Manufacturing, 29(7), pp. 754-767(2016).
    42. Balaman, Ş.Y., Matopoulos, A., Wright, D.G., et al. “Integrated optimization of sustainable supply chains and transportation networks for multi technology bio-based production: A decision support system based on fuzzy ε-constraint method”, Journal of cleaner production, 172, pp. 2594-2617 (2018).
    43. Yu, H., Wen, G., Gan, J., et al. “Self-paced learning for k-means clustering algorithm”, Pattern Recognition Letters, (2018).
Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 14 August 2021

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