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Hassan-pour, H., ghaffari Turan, H., zaree, M., mohammadi, A. (2020). Supplier selection and order allocation under uncertain conditions using evolutionary algorithms (case study: Isar general items support company). Electronic and Cyber Defense, 22(68), 92-108.
Hossein-ali Hassan-pour; hossein ghaffari Turan; mostafa zaree; ali mohammadi. "Supplier selection and order allocation under uncertain conditions using evolutionary algorithms (case study: Isar general items support company)". Electronic and Cyber Defense, 22, 68, 2020, 92-108.
Hassan-pour, H., ghaffari Turan, H., zaree, M., mohammadi, A. (2020). 'Supplier selection and order allocation under uncertain conditions using evolutionary algorithms (case study: Isar general items support company)', Electronic and Cyber Defense, 22(68), pp. 92-108.
Hassan-pour, H., ghaffari Turan, H., zaree, M., mohammadi, A. Supplier selection and order allocation under uncertain conditions using evolutionary algorithms (case study: Isar general items support company). Electronic and Cyber Defense, 2020; 22(68): 92-108.

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

مدیریت زنجیره تأمین
Volume 22, Issue 68, January 2021, Pages 92-108    PDF (2.15 M)
Document Type: Research/ Original/ Regular Article
Authors
Hossein-ali Hassan-pour* 1; hossein ghaffari Turan2; mostafa zaree3; ali mohammadi4
1Industrial Eng Group/ Imam Hussein Comprehensive University
2Manager of supply chain (amad) research group, logistics studies and research institute,
3Ph.D. Candidate of Industrial Engineering, University of Imam Hossein (PBUH)
4MSc. in Industrial Engineering, University of Imam Hossein (PBUH), Tehran
Receive Date: 25 May 2020,  Revise Date: 19 June 2020,  Accept Date: 20 July 2020 
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
Emergency Resilient Supply Chain; Nonlinear Integer Programming; Multi-Objective Optimization; NSGA-II; MOSPO
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