A Robust Optimization Model for Designing of Green Close Loop Supply Chain of Perishable Products

Document Type : Research Paper

Authors

1 M. A. student, Department of Industrial Engineering, Faculty of Industrial Engineering, Khajeh Nasiruddin Toosi University of Technology, Tehran, Iran

2 Assistant Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Khajeh Nasiruddin Toosi University of Technology, Tehran, Iran

3 Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Khajeh Nasiruddin Toosi University of Technology, Tehran, Iran

Abstract

Air pollution and CO2 emissions are important global issues, and reducing carbon emissions is one of the most important goals. In the supply chain of perishable products, delivery and distribution are significant issues, which many researchers have always paid attention to. In this research, we have used mixed integer linear programming to model the green supply chain, which includes two objective functions profit maximization and CO2 emission minimization. Due to the occurrence of uncertainties, cost and price are uncertain, we have used the robust optimization approach, and presented a new robust optimization model. This problem is included in NP-hard problems and the exact method is not efficient in large dimensions. The exact solution method of the constraint epsilon, and for the large-scale problems, meta-heuristic algorithms of NSGA, MOSA and MOPSO were used. In order to obtain the best and most accurate solutions in meta-heuristic algorithms, the parameters were adjusted with the help of Taguchi's design. According to the results of validity tests, the average difference with the optimal solution is 0.2 to 0.8% and in robust models, it is 0.0.7 to 0.9%, which is acceptable. The lowest execution time is related to the MOSA algorithm, NSGA and MOPSO respectively, and the efficiency of these algorithms is not significantly different according to the standard multi-objective criteria.

Keywords

References

  • Guide Jr, V.D.R. Van Wassenhove, L.N., 2006. Closed‐loop supply chains: an introduction to the feature issue (part 1). Production and Operations Management, 15(3), pp.345-350.
  • Hauser, W.M. and Lund, R.T., 2003. The remanufacturing industry: anatomy of a giant: a view of remanufacturing in America based on a comprehensive survey across the industry. Department of Manufacturing Engineering, Boston University.
  • قهرمانی نهر، جاوید. قدرت نما، علی. ایزد بخش، حمیدرضا. توکلی مقدم، رضا.1397. طراحی یک شبکه زنجیره‌تأمین سبز چندهدفه چندمحصولی وچنددوره‌ای با درنظر گرفتن تخفیف در شرایط عدم قطعیت. نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، ص 119-137.
  • Thierry, M., Salomon, M., Van Nunen, J. and Van Wassenhove, L., 1995. Strategic issues in product recovery management. California management review, 37(2), pp.114-136.
  • Mirmajlesi, S.R. and Shafaei, R., 2016. An integrated approach to solve a robust forward/reverse supply chain for short lifetime products. Computers & Industrial Engineering, 97, pp.222-239.
  • Taguchi, G., 1986. Introduction to quality engineering: designing quality into products and processes (illustrate). White Plains: The Organization.
  • Gen M, Cheng RW,1997. Genetic algorithms and engineering design. Wiley, New York.
  • Roghanian, E. and Cheraghalipour, A., 2019. Addressing a set of meta-heuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions. Journal of Cleaner Production, 239, p.118081.

 

  • Yavari, M. and Geraeli, M., 2019. Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of Cleaner Production, 226, pp.282-305.
  • Kannegiesser, M., Günther, H.O. and Autenrieb, N., 2015. The time-to-sustainability optimization strategy for sustainable supply network design. Journal of Cleaner Production, 108, pp.451-463.
  • Banasik, A., Kanellopoulos, A., Claassen, G.D.H., Bloemhof-Ruwaard, J.M. and van der Vorst, J.G., 2017. Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183, pp.409-420.
  • Cheraghalipour, A., Paydar, M.M. and Hajiaghaei-Keshteli, M., 2018. A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms. Applied Soft Computing, 69, pp.33-59.
  • Caixeta-Filho, J.V., 2006. Orange harvesting scheduling management: a case study. Journal of the Operational Research Society, 57(6), pp.637-642.
  • Gholamian, M.R. and Taghanzadeh, A.H., 2017. Integrated network design of wheat supply chain: A real case of Iran. Computers and Electronics in Agriculture, 140, pp.139-147.
  • Gerdrodbari, M.A., Harsej, F., Sadeghpour, M. and Aghdam, M.M., 2022. A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain. Journal of Industrial and Management Optimization, 18(5), p.3155.
  • Ghahremani Nahr, J., Pasandideh, S.H.R. and Niaki, S.T.A., 2020. A robust optimization approach for multi-objective, multi-product, multi-period, closed-loop green supply chain network designs under uncertainty and discount. Journal of industrial and production engineering, 37(1), pp.1-22.
  • Ghasemkhani, A., Tavakkoli-Moghaddam, R., Rahimi, Y., Shahnejat-Bushehri, S. and Tavakkoli-Moghaddam, H., 2022. Integrated production-inventory-routing problem for multi-perishable products under uncertainty by meta-heuristic algorithms. International Journal of Production Research, 60(9), pp.2766-2786.
  • Goodarzian, F., Shishebori, D., Bahrami, F., Abraham, A. and Appolloni, A., 2021. Hybrid meta-heuristic algorithms for optimising a sustainable agricultural supply chain network considering CO2emissions and water consumption. International Journal of Systems Science: Operations & Logistics, pp.1-30.
  • Abazari, S.R., Aghsami, A. and Rabbani, M., 2021. Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters. Socio-Economic Planning Sciences, 74, p.100933.
  • Al-Ashhab, M.S., 2022. A multi-objective optimization modelling for design and planning a robust closed-loop supply chain network under supplying disruption due to crises. Ain Shams Engineering Journal, p.101909.
  • Yantong, L.I., Feng, C.H.U., Zhen, Y.A.N.G. and Calvo, R.W., 2016. A production inventory routing planning for perishable food with quality consideration. Ifac-Papersonline, 49(3), pp.407-412.
  • Momeni, M.A., Jain, V., Govindan, K., Mostofi, A. and Fazel, S.J., 2022. A novel buy-back contract coordination mechanism for a manufacturer-retailer circular supply chain regenerating expired products. Journal of Cleaner Production, 375, p.133319.
  • Fasihi, M., Tavakkoli-Moghaddam, R., Najafi, S.E. and Hajiaghaei, M., 2021. Optimizing a bi-objective multi-period fish closed-loop supply chain network design by three multi-objective meta-heuristic algorithms. Scientia Iranica.
  • Song, L. and Wu, Z., 2022. An integrated approach for optimizing location-inventory and location-inventory-routing problem for perishable products. International Journal of Transportation Science and Technology.
  • Fasihi, M., Tavakkoli-Moghaddam, R., Najafi, S.E. and Hajiaghaei-Keshteli, M., 2021. Developing a bi-objective mathematical model to design the fish closed-loop supply chain. International Journal of Engineering, 34(5), pp.1257-1268.
  • Ghasemkhani, A., Tavakkoli-Moghaddam, R., Shahnejat-Bushehri, S., Momen, S. and Tavakkoli-Moghaddam, H., 2019. An integrated production inventory routing problem for multi perishable products with fuzzy demands and time windows. IFAC-PapersOnLine, 52(13), pp.523-528.
Journal of Industrial Engineering Research in Production Systems
Volume 10, Issue 20
January 0
Pages 113-131

Files

Share

How to cite

Statistics