Supplier selection and order allocation under uncertain conditions using evolutionary algorithms (case study: Isar general items support company)

Document Type : Research/ Original/ Regular Article

Authors

1 Industrial Eng Group/ Imam Hussein Comprehensive University

2 Manager of supply chain (amad) research group, logistics studies and research institute,

3 Ph.D. Candidate of Industrial Engineering, University of Imam Hossein (PBUH)

4 MSc. in Industrial Engineering, University of Imam Hossein (PBUH), Tehran

Abstract

Supply Chain Management is one of the new approaches in supply chain management and
a competitive advantage for organizations. The purpose of this research is to present a hybrid
model for supplier selection and order allocation under uncertainty in which a two-objective
nonlinear integer programming model is presented to optimize the general supply chain where
all parameters of objective functions and constraints are presented. It is considered uncertain.
The method of solving this is that the appropriate weights of the relevant metrics and sub-criteria
for the producers are first obtained by using the organization's criteria through the multi-criteria
decision making method. These weights provide input to the proposed mathematical model. The
mathematical model presented is solved as a case study using GAMS software. Then, two multiobjective
meta-heuristic algorithms, including genetic algorithm and particle optimization
algorithm with inappropriate sorting, are presented and their results are compared with accurate
software for validation. The responses of the meta-algorithms to the GAMS solutions for
different problems are less than 2.5%. These results show that the proposed algorithms converge
to the optimal and efficient solution

Keywords

References

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History

  • Receive Date: 25 May 2020
  • Revise Date: 19 June 2020
  • Accept Date: 20 July 2020
  • Publish Date: 17 December 2020

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