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, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.

2 Assistant Professor, Faculty of Basic Sciences, Engineering and Environment, University of Salford, Manchester, England.

3 Ph.D. student, Department of Industrial Management, Kish International Campus of Tehran University, Kish, Iran.

Abstract

Introduction and objectives: Sustainable development is defined as development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. It encompasses economic, social, and environmental dimensions that must be considered simultaneously. With the increasing importance of sustainable development, many companies worldwide are motivated, either proactively or reactively, to collect their used products. In such circumstances, establishing a reverse logistics network based on sustainable development is essential. The decision to outsource logistics has gained significance due to the need to avoid fixed costs, heavy investment, and achieve economic advantages, with many companies recognizing the potential benefits of high-quality logistics services.
Method: This research presents a mixed integer programming model for planning reverse logistics outsourcing in the assembly cycle of the automotive industry, focusing on a cost-oriented objective function. The research scope includes the assembly cycle of production lines, specifically prioritizing high-volume car manufacturers (light vehicles), and focuses on the Saipa Automotive Industrial Group, including the Ryan Saipa Leasing Group. The research period spans from 1389 to 1398 in the Iranian calendar. Variables such as non-commercial receivables, total assets, operating profit, net profit, and market value were evaluated using MATLAB software based on published statistics from Saipa.
Findings: The research findings indicate that among the variables of non-commercial receivables, total assets, operating profit, net profit, and market value, net profit to operating profit and sales (operating income) are of significant importance. The highest amount of non-commercial receivables for Saipa occurred in 1398 during the summer, while the highest total assets were recorded in 1392 during the summer. The highest operating profit was observed in 1398 during the winter, and the highest net profit was in 1390 during the spring. The degree of data convergence was calculated in the regression charts of sales (operating income) to operating profit and net profit to operating profit for the years 1389-1398. The degree of data convergence in the regression chart of sales to operating profit based on the conceptual model in 1398 was 0.9895, and for net profit to operating profit in 1398, it was 0.9961. The regression rate for the conceptual model in the test phase was 0.79, and in the overall processing stage, it was also 0.79. The histogram error rate was calculated for all three stages of learning, validation, and testing, with an error rate of 0.002375, which is acceptable due to its proximity to zero. Comparing these results with other studies shows an improvement in the regression and error rate in the analysis of the objective function.
Conclusion: Based on the calculated weight of the criteria in two-way assembly line balancing issues, it can be concluded that the decision team pays special attention to strategic issues in addition to production issues. The production rate of the line, which is the inverse of the production cycle time, affects the company's market share in the long term and increases its market share.

Keywords

Main Subjects

References

  1. Alkahtani, M., & et al. (2021). An Insight into Reverse Logistics with a Focus on Collection Systems. Sustainability, 8, 1-21.
  2. Arunodaya, R. M., Pratibha, R., & Kiran, P. (2021). Fermatean fuzzy CRITIC‑EDAS approach for the selection of sustainable third‑party reverse logistics providers using improved generalized score function. Journal of Ambient Intelligence and Humanized Computing, l9, 1-17.
  3. Bazargan, A., Ghasemi, R., Eftekhar Ardebili, M., & Zarei, M. (2017). The relationship between ‘higher education and training’and ‘business sophistication’. Iranian Economic Review, 21(2), 319-341.
  4. Chia-Nan, W., Thanh-Tuan, D., & Ngoc-Ai-Thy, N. (2021). Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach. Axioms, 10, 1-22.
  5. Christos, I. P. (2021). Measuring and eliminating the bullwhip in closed loop supply chains using control theory and Internet of Things. Annals of Operations Research, 7, 1-18.
  6. Cortés, P., Pascual, A., & Valero, F. (2017). Identification of reverse logistics decision types from mathematical models. Journal of Industrial Engineering and Management, 2, 1-12.
  7. Eng, L. L., & Vichitsarawong, Th. (2022). Comparing the usefulness of two profit subtotals: Operating income and earnings before interest and taxes. Finance Research Letters, 47, 103-115.
  8. Ghasemi, R., Alidoosti, A., Hosnavi, R., & Norouzian Reykandeh, J. (2018). Identifying and prioritizing humanitarian supply chain practices to supply food before an earthquake. Industrial management journal, 10(1), 1-16. [In Persian].
  9. Ghasemi, R., Hashemi–Petroudi, S. H., Mahbanooei, B., & Mousavi–Kiasari, Z. (2013). Relationship between Infrastructure and Technological Readiness based on Global Competitiveness Report: a Guidance for Developing Countries. 1 st International. In 7th national Conference on Electronic Commerce & Economy, 19-21.
  10. Gholizadeh, H., Goh, M., Fazlollahtabar, H., & Mamashli, Z. (2022). Modelling uncertainty in sustainable-green integrated reverse logistics network using metaheuristics optimization. Computers & Industrial Engineering, 163, 107-118.
  11. Hashemi, S. E. (2021). A fuzzy multi-objective optimization model for a sustainable reverse logistics network design of municioal waste-collecting considering the reduction of emissions. Journal of Cleaner Production, 318, 128-132.
  12. Jafarnejad, A., Ghasemi, R., Abdollahi, B., & Esmailzadeh, A. (2013). Relationship between macroeconomic environment and technological readiness: A secondary analysis of countries global competitiveness. The Journal of International Management Perspective, 1(2), 1-13.
  13. Jamalian, A., Ghadikolaei, A. S., Zarei, M., & Ghasemi, R. (2018). Sustainable supplier selection by way of managing knowledge: a case of the automotive industry. International Journal of Intelligent Enterprise, 5(1-2), 125-140.
  14. Kamali Mohammadzadeh, A., & et al. (2018). A Fuzzy Analytic Network Process (FANP) approach for prioritizing internet of things challenges in Iran. Technology in Society, 53, 124-134.
  15. Kim, S.T., Lee, H. H. & Hwang, T. (2020). Logistics integration in the supply chain: a resource dependence theory perspective. International Journal of Quality Innovation, 6(5), 1-14.
  16. Mardani, A., Kannan, D., & Hooker, R. (2019). Evaluating of Green and Suatainable Supply Chain Management Using Application of Structural Equation Modelling. Journal of Cleaner Production, 15, 1-19.
  17. Marta Starostka , P. (2021). The use of information systems to support the managenet of reverse logistics processes. Intellient Information and Engineering System, 192, 2586-2595.
  18. Mishra, A.R., Rani, P. & Pandey, K. (2021). Fermatean fuzzy CRITIC EDAS approach for the selection of sustainable third party reverse logistics providers using improved generalized score function. Journal of Ambient Intelligence and Humanized Computing, 13, 295-311.
  19. Mohaghar, A., Ghasemi, R., Abdullahi, B., Esfandi, N., & Jamalian, A. (2011). Canonical correlation analysis between supply chain relationship quality and cooperative strategy: a case study in the Iranian automotive industry. European Journal of Social Sciences, 26(1), 132-145.
  20. Mohaghar, A., Mahbanooei, B., Behnam, M., & Khavari, Z. (2018). Analyzing OECD's Labor Market Efficiency in 2018. Economic and Social Development. Book of Proceedings, 341-353.
  21. Mohaghar, A., Sadeghi Moghadam, M. R., Ghourchi Beigi, R., & Ghasemi, R. (2021). IoT-based services in banking industry using a business continuity management approach. Journal of Information Technology Management, 13(4), 16-38.
  22. Motevalli Haghighi, S., Torabi, S. A., & Ghasemi, R. (2016). An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). Journal of cleaner production, 137, 579-597.
  23. Nasrollahi, M., Ghadikolaei, A. S., Ghasemi, R., Sheykhizadeh, M., & Abdi, M. (2022). Identification and prioritization of connected vehicle technologies for sustainable development in Iran. Technology in Society, 68, 101829.
  24. Olumide, E. O., Swapnil, B., Fabio, S., & Jan, O. S. (2021). Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study. Journal of Intelligent Manufacturing, 3, 1-8.
  25. Papanagnou, C. I. (2021). Measuring and eliminating the bullwhip in closed loop supply chains using control theory and Internet of Things. Annals of Operations Research, 310, 153-170.
  26. Rastegar, A. A., Mahbanooei, B., & Ghasemi, R. (2012). Canonical correlation analysis between technological readiness and labor market efficiency: A secondary analysis of countries global competitiveness in 2011–2012. In 13th International Conference on Econometrics, Operations Research and Statistics, 24-26.
  27. Razavi, S. M., Abdi, M., Amirnequiee, S., & Ghasemi, R. (2016). The impact of supply chain relationship quality and cooperative strategy on strategic purchasing. Journal of Logistics Management, 5(1), 6-15.
  28. Riaz, M., Farid, H. M. A., Aslam, M., Pamucar, D., & Bozanic, D. (2021). Novel Approach for Third-Party Reverse Logistic Provider Selection Process under Linear Diophantine Fuzzy Prioritized Aggregation Operators. Symmetry , 13(7), 1152
  29. Sichao, L., Geng, Zh., & Lihui, W. (2018). IoT-enabled Dynamic Optimisation for Sustainable Reverse Logistics. Procedia CIRP, 1-10.
  30. Škrjanc, I., & et al. (2019), Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey. Information Sciences, 490, 344-368.
  31. Starostka -Patyak, M. (2021). The use of information systems to support the managenet of reverse logistics processes. Procedia Computer Science, 192, 2586-2595.
  32. Taghizadeh Yazdi, M., & Salmani Zarchi, E. (2018), Presenting a multi-objective model of a multi-level, multi-product green closed-loop supply chain with a classical weighted sum approach: Proto Front production (Study case: Shahpar Mumtaz Shoe Company). The Journal of Industrial management perspective, 36, 107-137. [In Persian]
  33. Tavan, F., Sajadi, S.M., Movahedi Sobhani, F., & Azizi, A. (2023), A Model of Simulation-Data Envelopment Analysis in Network Failure Manufacturing Systems Considering Reliability Centered Maintenance and Return of Defective Items. The Journal of Industrial Management Perspective, 13, 119-157. [In Persian]
  34. Zadtootaghaj, P., Mohammadian, A., Mahbanooei, B., & Ghasemi, R. (2019). Internet of Things: A Survey for the Individuals' E-Health Applications. Journal of Information Technology Management, 11(1), 102-129.
  35. Zarbakhshnia, N., Wu, Y., Govindan, K., & Soleimani, H.  (2020). A novel hybrid multiple attribute decision-making approach for outsourcing sustainable reverse logistics. Journal of Cleaner Production, 242, 1-11.
  36. Zarei, M., Jamalian, A., & Ghasemi, R. (2017). Industrial guidelines for stimulating entrepreneurship with the internet of things. The Internet of Things in the Modern Business Environment,147-166.
  37. Zarei, M., Mohammadian, A., & Ghasemi, R. (2016). Internet of things in industries: A survey for sustainable development. International Journal of Innovation and Sustainable Development, 10(4), 419-442.
  38. Zhang, S., & Zhao, X. (2015). Fuzzy Robust Control for an Uncertain Switched Dual-Channel Closed-Loop Supply Chain Model. IEEE Transaction on Fuzzy System, 23(3), 1-19.
  39. Zhang, X., Li, Z., & Wang, Y. (2020). A Review of the Criteria and Methods of Reverse Logistics Supplier Selection. Processes, 2, 1-17.

Files

Share

How to cite

Statistics