Supplier selection and order allocation model with disruption and environmental risks in centralized supply chain

Abstract

Supply chain managers have realized that competition between supply chains has replaced competition between companies. In addition, with increasing disruptions and uncertainty in planning, companies need to be able to make informed decisions at risk. Coordination in the resilient supply chain and appropriate selection of suppliers play a key role in risky situations. In previous research, mainly the impact of resilience strategies in the decentralized supply chain has been investigated and ignored the reliability of suppliers in the decision-making process. Therefore, we provide an effective framework for selecting reliable suppliers and order allocation, which increases the supply chain's benefits by considering the risk reduction strategies and coordination between the buyer and the supplier. Thus, we optimized the problem of supplier selection and order allocation in a centralized supply chain using mixed-integer nonlinear programming models and risk reduction strategies. These strategies are protected suppliers, back-up suppliers, reserving additional capacity, emergency stock, and geographical separation. Also, by considering the failure mode and effects analysis technique and the risk priority number constraint, suppliers' reliability has been considered. A numerical example is solved with the exact method. In addition, the application of the proposed models in a case study has been investigated by the Grasshopper optimization algorithm. Based on the sensitivity analysis results, we found that the simultaneous use of risk reduction strategies in the models significantly reduces supply chain costs and increases its benefits. Also, considering the reliability constraints causes supply chain managers to choose suppliers with more desirable reliability.

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Correspondence to Masoud Rabbani.

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Appendices

Appendix 1

See Table

Table 21 The value of the parameters in small sizes

21

Appendix 2

See Tables

Table 22 Pairwise comparison matrix of FMEA risk factors of first DM

22,

Table 23 Pairwise comparison matrix of risk sub-factors with regard to severity of first DM

23,

Table 24 Pairwise comparison matrix of risk sub-factors with regard to occurrence of first DM

24,

Table 25 Pairwise comparison matrix of risk sub-factors with regard to detection of first DM

25,

Table 26 Final weights of suppliers with regard to risk sub-factors of first DM

26,

Table 27 Pairwise comparison matrix of FMEA risk factors of second DM

27,

Table 28 Pairwise comparison matrix of risk sub-factors with regard to severity of second DM

28,

Table 29 Pairwise comparison matrix of risk sub-factors with regard to occurrence of second DM

29,

Table 30 Pairwise comparison matrix of risk sub-factors with regard to detection of second DM

30,

Table 31 Final weights of suppliers with regard to risk sub-factors of second DM

31,

Table 32 Pairwise comparison matrix of FMEA risk factors of third DM

32,

Table 33 Pairwise comparison matrix of risk sub-factors with regard to severity of third DM

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Table 34 Pairwise comparison matrix of risk sub-factors with regard to occurrence of third DM

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Table 35 Pairwise comparison matrix of risk sub-factors with regard to detection of third DM

35,

Table 36 Final weights of suppliers with regard to risk sub-factors of third DM

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Appendix 3

See Tables

Table 37 The value of the parameters in case study

37,

Table 38 The value of the parameters in case study

38,

Table 39 The value of the \((C_{ij} ,\alpha_{ij} ,e_{ij} ) \times 10^{3}\) parameters in case study

39

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Rezaei, A., Aghsami, A. & Rabbani, M. Supplier selection and order allocation model with disruption and environmental risks in centralized supply chain. Int J Syst Assur Eng Manag (2021). https://doi.org/10.1007/s13198-021-01164-1

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Keywords

  • Resilient supply chain
  • Supplier selection and order allocation
  • Disruption risks
  • Nonlinear mixed-integer programming
  • Failure mode and effects analysis
  • Grasshopper optimization algorithm