Robust Integrated Optimization for Green Closed Loop Supply Chain

Document Type : Original Article

Authors

1 M.Sc., Mazandaran University of Science and Technology.

2 Assistant Professor, Mazandaran University of Science and Technology.

3 Associate Professor, Mazandaran University of Science and Technology.

Abstract

With increasing environmental pollution in recent years, researchers have focused on designing a closed loop supply chain network with consideration of environmental issues. In this research an uncertain bi-objective, multi-period, multi-product and multi-level closed-loop supply chain network is presented. Uncertainty in demand, transportation costs are considered and to counteract this uncertainty the robust optimization approach is used. The proposed supply chain network consists of four levels of forward supply chain and four levels of reverse chain. The proposed model is a mixed integer linear programming (MILP) model with the aim maximizing profit and minimizing generated pollution by transportation of products, and operational centers. The proposed model is solved by lingo software, so that the multi-objective model has been handled by utility based goal programming method. Finally, the results are analyzed and the comparison of different scenarios indicates that the objective function has strongly shown the uncertainty parameters and the effect of uncertainty in the parameters simultaneously. Therefore, network modeling based on different scenarios can be a good tool for deciding on confrontation with uncertain and ambiguous parameters.

Keywords

References

1. A Ben-Tal, L El Ghaoui, A Nemirovski. (2009). Robust optimization. Princeton University Press.
2. Alam Tabriz, A., Roghanian, E, Hosseinzadeh, M.  (2011). Design and Optimization of Reverse Logistics Network under Uncertainty Conditions Using Genetic Algorithm. Industrial Management Perspective, 1, 89-61 (In Persian).
3. C-T Chang. (2011). Multi-choice goal programming with utility functions. Eur. J. Oper. Res. 215, 439–445.
4. Diabat, A., Abdallah T., Al-Refaie, A., Svetinovic, D., & Govindan, K. (2013). Strategic closed-loop facility location problem with carbon market trading. IEEE Transactions on Engineering Management, 60, 398-408.
5. Fakhrzad, M.B, Talebzadeh P, & Goodarzian, F. (2018). Mathematical Formulation and Solving of Green Closed-loop Supply Chain Planning Problem with Production, Distribution and Transportation Reliability. International Journal of Engineering, 31(12), 2059-2067.
6. Farahani, R.Z., Rezapour, S., Drezner, T., et al. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92–118.
7. Garg, k., Kannan, D., Diabat, A., & Jha, P.C. (2015). A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. Jornal of Cleaner production, 100, 297-314.
8. Kannan, G., Sasikumar, P., Devika, K. (2010). A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied Mathematical Modelling, 34(3), 655-670.
9. Listeş. O., & Dekker, R. (2005). A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, 160(1), 268-287.
10. Bashiri, M., & Moslemi, A. (2012). A Robust Scenario Based Approach in an Uncertain Condition Applied to Location-Allocation Distribution Centers Problem. International Journal of Management and Business Research, 1, 199-210.
11. Mohammadi, A., Alam Tabriz, A., Pishvaei, M. (2018). A Model for Mainstreaming the Sustainable supply Chain Considering the Consistency of Financial and Physical Flow. Industrial Management Perspective, 29, 62-39.
12. Mohammadi, S., & Mohammadi, A. (2014).Stochastic scenario-based model and investigating size of battery energy storage and thermal energy storage for micro-grid. International Journal of Electrical Power & Energy Systems, 61, 531-546.
13.Mortazavi, S.D., Seif-Barghi, M. (2018). Bi-objective modeling of allocation problem in a green supply chain considering the transport system and CO2 emissions. Industrial Management Perspective, 29, 185- 163 (In Persian).
14. Mulvey, J.M., & Ruszczynski, A. (1995).A new scenario decomposition method for large scale stochastic optimization. Operations Research, 43, 477–490.
15. Mulvey, J.M., Vanderbei, R.J., & Zenios, S.A. (1995). Robust optimization of large scale systems. Oper. Res, Lett, 43(2), 264–281.
16. Naderi, M.J., & Pishvaee M.S. (2017). A stochastic programming approach to integrated water supply and wastewater collection network design problem. Computers & Chemical Engineering.
17. Niknam, T., Azizipanah-Abarghooee, R., & Narimani, M.R. (2012). Ane cient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation. Applied Energy, 99, 455-470.
18. Pedram. A., Bin Yusoff, N., Udoncy, O.E., Mahat, A.B., & Pedram, P. (2017). Integrated forward and reverse supply chain: A tire case study. Waste Management.
19. Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Appl. Math. Model, 37(1), 328-344.
20. Saedinia R, Vahdani B , Etebari F , Nadjafi B.A. (2019). Robust gasoline closed loop supply chain design with redistricting, service sharing and intra-district service transfer. Transportation Research Part E: Logistics and Transportation Review, 123, 121-141.
21. Safaei, A.B., Roozbeh, A., & Paydar, M.M. (2017). A Robust optimization model for the design of a cardboard closed-loop supply chain.Cleaner Production.
22. Soleimani, H., Govindan, K., Saghafi, H.J. & Afari, H. (2017). Fuzzy Multi-Objective Sustainable and Green Closed-Loop supply chain Network design. Computers & industrial engineering, 8352(17), 30184-5
23. Su, T-S. (2014). Fuzzy multi-objective recoverable remanufacturing planning decisions involving multiple components and multiple machines. Computers & Industrial Engineering, 72, 72-83.
24. Wang, H.F., & Hsu H. W. (2010).A closed-loop logistic model with a spanning-tree based genetic algorithm. Computers & operations research, 37(2), 376-389.
25. Zabinsk o.h.m.s.z.b.(2010). Stochastic optimization of medicial supply location and distribution in disaster management. International journal of production economics, 126, 76-84.
26. Zikopoulos, C. & Tagaras, G. (2015). Reverse supply chains: Effects of collection network and returns classification on profitability. European Journal of Operational Research, 246(2), 435-449.
27. Zhan, S-L. & Liu., N. (2011). A multi-objective stochastic programming model for emergency logistics based on goal programming.in Computational Sciences and Optimization (CSO). 2011Fourth International Joint Conference on IEEE.
Journal of Industrial Management Perspective
Volume 9, Issue 3 - Serial Number 35
December 2019
Pages 55-85

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