Reverse Logistics Outsourcing Planning Model Based on Intuitive Fuzzy Analysis Considering Artificial Intelligence Methods (Case Study: Saipa Company)

Document Type : Original Article

Authors

1 Professor, University of Tehran.

2 Assistant Professor, University of Salford.

3 PhD student, Kish International Campus of Tehran University.

10.48308/jimp.2023.232882.1484

Abstract

The research has focused on providing a reverse logistics outsourcing planning model in the assembly cycle of the automotive industry based on a cost-oriented objective function. Based on the knowledge of the identified dimensions and components of the process of reverse logistics outsourcing based on Logistics 4.0 and by studying the subject literature and presenting a proposed method based on the intuitive fuzzy model and artificial intelligence, conduct statistical measures and ultimately measure and comprehensively evaluate the amount of outsourcing in the considered logistic model is based on the intended statistical population. The two main criteria are quality and cost. The subject area is to present a reverse logistics supply network model based on artificial intelligence methods in the context of the Internet of Things and in the automotive industry. The research area is in the supply chain of Saipa Automotive Industrial Group and its sub-group. The time domain of the research is in a specific period from 1395 to 1398. The variables used include non-commercial receivables, total assets, operating profit, net profit and market value, which have been evaluated through the published statistics of Saipa Company. The degree of data convergence in the regression chart of sales (operating income) to operating profit based on the conceptual model in 1998 is equal to 0.9895 and the regression chart of net profit to operating profit in 1998 based on the considered conceptual model is equal to 09961.

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Articles in Press, Accepted Manuscript
Available Online from 15 November 2023
  • Receive Date: 24 August 2023
  • Revise Date: 29 September 2023
  • Accept Date: 07 November 2023
  • First Publish Date: 15 November 2023
  • Publish Date: 15 November 2023