Development of Multi Objective Multi Period Closed-Loop Supply Chain Network Model Considering Uncertain Demand and Capacity

Document Type : Original Article

Authors

1 *Ph.D. student, Departmant of Management, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

2 Assistant Professor, Departmant of Management, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran.

3 Assistant Professor, Department of Industrial Engineering, University of Kurdistan.

4 Assistant Professor, Department of Industrial Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

Abstract

     Today, the discussion about the reuse of consumer products has particular importance. Since the closed loop supply chain is not only streaming but also includes reverse flow, companies are successful that integrate between direct and reverse supply chain. This paper model is multi-objective, multilevel, multi-disciplinary, and single-product in uncertain conditions. The objective functions of the model include minimizing costs, increasing the revenues from the recycled product, reducing the negative environmental effects of production, transportation and recycling of the product. To solve the problem, the approach TH, which is a method for converting multi-objective functions to single-objective, has been used. Numerical examples have been designed and solved for validating the proposed model. To study the application of the model, a case study was conducted on trolleys product in one of the hospitals industry companies in Tehran. To assess the effect of changes in the parameters affecting the improvement of objectives, sensitivity analysis on budget parameters, production capacity and uncertainty coefficient have been made. The results show the significant impact of production and budget on increasing the profit from recycled parts as well as the effect of fuzzy demand coefficient on the objective of cost and environmental effects which is increasing.

Highlights

  1. Afarin, A., Rezaei, A. Khajeh Rezaei, A. (2015). Presentation of a mathematical model for closed loop supply chain in the process of product recycles, International Conference on Management, Economics and Industrial Engineering (In Persian).
  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. Al-Salem, M., Diabat, A., Dalalah, D., & Alrefaei, M. (2016). A closed-loop supply chain management problem: Reformulation and piecewise linearization.Journal of Manufacturing Systems, 40, 1-8.
  4. Amin, S. H., Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165.
  5. Bashiri, M., Shiri, M. (2015). Designing the closed-loop network considering a multi-part collecting center under uncertainty conditions.Journal of Industrial Engineering Research in Manufacturing Systems, 5, 27-41(In Persian).
  6. Behmanesh, E. Pannek, J. (2016). Modeling and random path-based direct encoding for a closed loop supply chain model with flexible delivery paths, IFAC-Papers On Line, 49)2(, 078-083.
  7. Chen, Y. T., Chan, F. T., Chung, S. H., & Park, W. Y. (2018). Optimization of product refurbishment in closed-loop supply chain using multi-period model integrated with fuzzy controller under uncertainties.Robotics and Computer–Integrated Manufacturing, 50, 1-12.
  8. Coyle, J. Langley, J.Jr. Gibson, B., Novack, R., & Bardi, E. (2009). Managing supply chains:a logistics perspective (8th ed.). Mason, OH: South Western.
  9. Cui, Y. Y., Guan, Z., Saif, U., Zhang, L., Zhang, F., & Mirza, J. (2017). Close Loop Supply Chain Network Problem with Uncertainty in Demand and Returned Products: Genetic Artificial Bee Colony Algorithm Approach.Journal of Cleaner Production, 162, 717-742
  10. Dai, Z., & Zheng, X. (2015).Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: a fuzzy and chance-constrained programming model. Computers & Industrial Engineering, 88, 444-457
  11. Doozandeh, A., Honarvar, M. (2014). Focused optimization for the inventory control model of corrupt commodities in the closed loop supply chain with the simultaneous generation and reproduction cycle, Second National Conference on Industrial Engineering and Sustainable Management (In Persian).
  12. Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2018). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty.Fuzzy Sets and Systems, 341, 69-91
  13. Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). A stochastic multi-objective model for a closed-loop supply chain with environmental considerations, Applied Soft Computing, 69, 232-249
  14. Ghaleb Loo, S., Tarokh, M.J. (2015). Design of Integrated Direct and Inverse Ethernet Supply Chain Network.Industrial Engineering Journal, 49(1), 93-106 (In Persian).
  15. Javid, A.A., Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design, 46(5), 582-597.
  16. Jayant, A., Gupta, P., & Garg, S. K. (2014). Simulation Modeling and Analysis of Network Design for Closed Loop Supply Chain: A Case Study of Battery Industry.Procedia Engineering, 97(1672), 2213-2221.
  17. Kadambala, D. K., Subramanian, N., Tiwari, M. K., Abdulrahman, M., & Liu, C. (2017). Closed loop supply chain networks: Designs for energy and time value efficiency.Int. J. Production Economics, 183, 382-393.
  18. Kafa, N., Hani, Y., & El Mhamedi, A. (2015). An integrated sustainable partner selection approach with closed-loop supply chain network configuration IFAC-Papers on Line, 48(3), 1840-1845.
  19. Kaya, O., & Urek, B. (2016).A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain.Computers & Operations Research,65, 93-103.
  20. Luis, J., ZeballosCarlos, A., MéndezAna, P., &Barbosa,p.(2018). Integrating decisions of product and closed-loop supply chain design under uncertain return flows.Computers & Chemical Engineering, 112, 211-238.
  21. Mohammadi, A. S., 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 (In Persian).
  22. 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).
  23. Notash, M., Zandyeh, M.,& Dorri, B. (2014). Multi-objective design of supply chain network with genetic algorithm approach.Management researches in Iran, 12, 183-207 (In Persian).
  24. Özceylan, E., & Paksoy, T. (2013).A mixed integer programming model for a closed-loop supply chain network. International Journal of Production Research, 51 (3), 718-734.
  25. Özceylan, E., Paksoy, T., & Bektaş, T. (2014). Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. Transportation Research Part E: logistics and transportation review, 61, 142-164
  26. Paksoy, T. Bektas, T. Ozceylan, E. (2011). Operational and environmental performance measures in a multi-product closed-loop supply chain. Transportation Research Part E, 47(4), 532-546.
  27. Paydar, M. M., Babaveisi, V., and Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk.Computers and Chemical Engineering, 104, 38-55.
  28. Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.
  29. Pishvaz, M.S., &Torabi, S.A. (2010). A possibilistic programming approach for closed- loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
  30. Ruimin, M. A., Lifei, Y. A. O., Maozhu, J. I. N., Peiyu, R. E. N., & Zhihan, L. V. (2016). Robust environmental closed-loop supply chain design under uncertainty, Chaos, Solitons and Fractals, 89, 195-202
  31. Sabri, E.H., Beamon, B.M. (2000).A multi-objective approach to simultaneous strategicand operational planning in supply chain design; Omega, 28, 581-598.
  32. Saffar, M.M., Shakouri Ganjavi, H, Razmi, j. (2015). Designs the supply chain of green closed loop with consideration of operating risks in uncertainty conditions. Journal of Industrial Engineering, 49(1), 55-68(In Persian).
  33. Sanei, M., Tavakoli Moghadam, R. (2014). Bi-objective Modeling for closed loop supply chain with shared risk and uncertain demand.Supply Chain Management, 43(16), 4-15 (In Persian).
  34. Sidi, M., Soltani, H. (2014). Designing a closed loop supply chain network using a fuzzy hierarchical analysis technique for waste management, the first national conference on industrial engineering research (In Persian).
  35. Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990-4012.
  36. Soltani Tehrani, M., Hassanpour, H. Ramezani, S. (2015). Bi-objective optimization model for cost and carbon dioxide in closed loop supply chain.Management researches in Iran, 19(1), 169-189(In Persian).
  37. Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015). Designing an environmentally conscious tire closed-loop supply chain network with multiple recovery options using interactive fuzzy goal programming.Applied Mathematical Modelling, (39), 2661-2702.
  38. Tahmasebi, H.A., Raheb, M, Jafari, S. (2018). Presented and solved a green optimization model in the closed-loop supply chain with the aim of increasing profit and reducing environmental problems, taking into account the warranty period of the product.Journal of Investigating Operations in its Applications, 15(3), 27-44(In Persian).
  39. Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S.(2015). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry.Journal of Cleaner Production, 113, 662-673.
  40. Tao, Z. G., Guang, Z. Y., Hao, S., & Song, H. J. (2015).Multi-period closed-loop supply chain network equilibrium withcarbon emission constraints, Resources, Conservation and Recycling, 104, 345-365.
  41. Tavakoli Moghadam, R. Rekavandy Omidi, M. Ghodrat Nema, A. (2013).Mathematical modeling to design integrated forward and reverse logisticsnetwork, Management research in Iran, 17.
  42. Tiwari, A., Chang, P. C., Tiwari, M. K., & Kandhway, R. (2016).A Hybrid Territory Defined Evolutionary Algorithm approach for Closed Loop Green Supply chain NetworkDesign. Computers&IndustrialEngineering, 99, 432-447.
  43. Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214.
  44. Vahdani, B., Razmi, J., Tavakkoli-Moghadam, R., (2012).Fuzzy possibilistic modeling forClosed loop recycling collection networks.Environmental Modeling & Assessment,17(6), 623-637.
  45. Xie L, Ma J. (2016). Study the complexity and control of the recycling-supply chain of China’s color TVs market based on the government subsidy. Communications in Nonlinear Science and Numerical Simulation, (38), 102-116.
  46. Zohal, M., & Soleimani, H. (2016). Developing an ant colony approach for green closed-loop supply chain network design.Journal of Cleaner Production, 133, 314-337.
  47. Zohori, S., Karimi, B, Mihami, R. (2016). Controlling the inventory of corrupt commodities in the closed loop supply chain, taking into account random demand. Journal of Industrial Engineering, 50(3), 429-439 (In Persian).

Keywords

References

  1. Afarin, A., Rezaei, A. Khajeh Rezaei, A. (2015). Presentation of a mathematical model for closed loop supply chain in the process of product recycles, International Conference on Management, Economics and Industrial Engineering (In Persian).
  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. Al-Salem, M., Diabat, A., Dalalah, D., & Alrefaei, M. (2016). A closed-loop supply chain management problem: Reformulation and piecewise linearization.Journal of Manufacturing Systems, 40, 1-8.
  4. Amin, S. H., Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165.
  5. Bashiri, M., Shiri, M. (2015). Designing the closed-loop network considering a multi-part collecting center under uncertainty conditions.Journal of Industrial Engineering Research in Manufacturing Systems, 5, 27-41(In Persian).
  6. Behmanesh, E. Pannek, J. (2016). Modeling and random path-based direct encoding for a closed loop supply chain model with flexible delivery paths, IFAC-Papers On Line, 49)2(, 078-083.
  7. Chen, Y. T., Chan, F. T., Chung, S. H., & Park, W. Y. (2018). Optimization of product refurbishment in closed-loop supply chain using multi-period model integrated with fuzzy controller under uncertainties.Robotics and Computer–Integrated Manufacturing, 50, 1-12.
  8. Coyle, J. Langley, J.Jr. Gibson, B., Novack, R., & Bardi, E. (2009). Managing supply chains:a logistics perspective (8th ed.). Mason, OH: South Western.
  9. Cui, Y. Y., Guan, Z., Saif, U., Zhang, L., Zhang, F., & Mirza, J. (2017). Close Loop Supply Chain Network Problem with Uncertainty in Demand and Returned Products: Genetic Artificial Bee Colony Algorithm Approach.Journal of Cleaner Production, 162, 717-742
  10. Dai, Z., & Zheng, X. (2015).Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: a fuzzy and chance-constrained programming model. Computers & Industrial Engineering, 88, 444-457
  11. Doozandeh, A., Honarvar, M. (2014). Focused optimization for the inventory control model of corrupt commodities in the closed loop supply chain with the simultaneous generation and reproduction cycle, Second National Conference on Industrial Engineering and Sustainable Management (In Persian).
  12. Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2018). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty.Fuzzy Sets and Systems, 341, 69-91
  13. Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). A stochastic multi-objective model for a closed-loop supply chain with environmental considerations, Applied Soft Computing, 69, 232-249
  14. Ghaleb Loo, S., Tarokh, M.J. (2015). Design of Integrated Direct and Inverse Ethernet Supply Chain Network.Industrial Engineering Journal, 49(1), 93-106 (In Persian).
  15. Javid, A.A., Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design, 46(5), 582-597.
  16. Jayant, A., Gupta, P., & Garg, S. K. (2014). Simulation Modeling and Analysis of Network Design for Closed Loop Supply Chain: A Case Study of Battery Industry.Procedia Engineering, 97(1672), 2213-2221.
  17. Kadambala, D. K., Subramanian, N., Tiwari, M. K., Abdulrahman, M., & Liu, C. (2017). Closed loop supply chain networks: Designs for energy and time value efficiency.Int. J. Production Economics, 183, 382-393.
  18. Kafa, N., Hani, Y., & El Mhamedi, A. (2015). An integrated sustainable partner selection approach with closed-loop supply chain network configuration IFAC-Papers on Line, 48(3), 1840-1845.
  19. Kaya, O., & Urek, B. (2016).A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain.Computers & Operations Research,65, 93-103.
  20. Luis, J., ZeballosCarlos, A., MéndezAna, P., &Barbosa,p.(2018). Integrating decisions of product and closed-loop supply chain design under uncertain return flows.Computers & Chemical Engineering, 112, 211-238.
  21. Mohammadi, A. S., 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 (In Persian).
  22. 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).
  23. Notash, M., Zandyeh, M.,& Dorri, B. (2014). Multi-objective design of supply chain network with genetic algorithm approach.Management researches in Iran, 12, 183-207 (In Persian).
  24. Özceylan, E., & Paksoy, T. (2013).A mixed integer programming model for a closed-loop supply chain network. International Journal of Production Research, 51 (3), 718-734.
  25. Özceylan, E., Paksoy, T., & Bektaş, T. (2014). Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. Transportation Research Part E: logistics and transportation review, 61, 142-164
  26. Paksoy, T. Bektas, T. Ozceylan, E. (2011). Operational and environmental performance measures in a multi-product closed-loop supply chain. Transportation Research Part E, 47(4), 532-546.
  27. Paydar, M. M., Babaveisi, V., and Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk.Computers and Chemical Engineering, 104, 38-55.
  28. Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.
  29. Pishvaz, M.S., &Torabi, S.A. (2010). A possibilistic programming approach for closed- loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
  30. Ruimin, M. A., Lifei, Y. A. O., Maozhu, J. I. N., Peiyu, R. E. N., & Zhihan, L. V. (2016). Robust environmental closed-loop supply chain design under uncertainty, Chaos, Solitons and Fractals, 89, 195-202
  31. Sabri, E.H., Beamon, B.M. (2000).A multi-objective approach to simultaneous strategicand operational planning in supply chain design; Omega, 28, 581-598.
  32. Saffar, M.M., Shakouri Ganjavi, H, Razmi, j. (2015). Designs the supply chain of green closed loop with consideration of operating risks in uncertainty conditions. Journal of Industrial Engineering, 49(1), 55-68(In Persian).
  33. Sanei, M., Tavakoli Moghadam, R. (2014). Bi-objective Modeling for closed loop supply chain with shared risk and uncertain demand.Supply Chain Management, 43(16), 4-15 (In Persian).
  34. Sidi, M., Soltani, H. (2014). Designing a closed loop supply chain network using a fuzzy hierarchical analysis technique for waste management, the first national conference on industrial engineering research (In Persian).
  35. Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990-4012.
  36. Soltani Tehrani, M., Hassanpour, H. Ramezani, S. (2015). Bi-objective optimization model for cost and carbon dioxide in closed loop supply chain.Management researches in Iran, 19(1), 169-189(In Persian).
  37. Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015). Designing an environmentally conscious tire closed-loop supply chain network with multiple recovery options using interactive fuzzy goal programming.Applied Mathematical Modelling, (39), 2661-2702.
  38. Tahmasebi, H.A., Raheb, M, Jafari, S. (2018). Presented and solved a green optimization model in the closed-loop supply chain with the aim of increasing profit and reducing environmental problems, taking into account the warranty period of the product.Journal of Investigating Operations in its Applications, 15(3), 27-44(In Persian).
  39. Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S.(2015). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry.Journal of Cleaner Production, 113, 662-673.
  40. Tao, Z. G., Guang, Z. Y., Hao, S., & Song, H. J. (2015).Multi-period closed-loop supply chain network equilibrium withcarbon emission constraints, Resources, Conservation and Recycling, 104, 345-365.
  41. Tavakoli Moghadam, R. Rekavandy Omidi, M. Ghodrat Nema, A. (2013).Mathematical modeling to design integrated forward and reverse logisticsnetwork, Management research in Iran, 17.
  42. Tiwari, A., Chang, P. C., Tiwari, M. K., & Kandhway, R. (2016).A Hybrid Territory Defined Evolutionary Algorithm approach for Closed Loop Green Supply chain NetworkDesign. Computers&IndustrialEngineering, 99, 432-447.
  43. Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214.
  44. Vahdani, B., Razmi, J., Tavakkoli-Moghadam, R., (2012).Fuzzy possibilistic modeling forClosed loop recycling collection networks.Environmental Modeling & Assessment,17(6), 623-637.
  45. Xie L, Ma J. (2016). Study the complexity and control of the recycling-supply chain of China’s color TVs market based on the government subsidy. Communications in Nonlinear Science and Numerical Simulation, (38), 102-116.
  46. Zohal, M., & Soleimani, H. (2016). Developing an ant colony approach for green closed-loop supply chain network design.Journal of Cleaner Production, 133, 314-337.
  47. Zohori, S., Karimi, B, Mihami, R. (2016). Controlling the inventory of corrupt commodities in the closed loop supply chain, taking into account random demand. Journal of Industrial Engineering, 50(3), 429-439 (In Persian).

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