Sustainable Tire Closed-Loop Supply Chain Design under Uncertain Return and Demand

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Information technology and Operations Management, Kharazmi University, Iran, Tehran.

2 Lecturer in Business Analytics, Queen’s University Belfast, Belfast, UK

3 The business school, Edinburgh Napier University, Edinburgh, UK

4 MSc, Operations Research, Kharazmi University, Iran, Tehran.

Abstract

Each year, millions of tires that have reached their end of life are either buried or burned, both of which harm the environment through polluting the air and groundwater. Companies need to consider their social responsibility, including employment and regional development, and the environmental impact of their activities when making strategic and operational decisions. This study addresses the closed-loop supply chain (CLSC) design problem with regard to the dimensions of sustainability. The options of retreading, recycling, and energy recovery, along with the use of green technologies are considered to minimize environmental impacts. The proposed decision approach uses Life cycle assessment (LCA)-based social indicators to model its social impacts, along with the use of eco-indicator 99 as a method of assessing environmental impacts. The developed mathematical model turns out to be a multi-objective, mixed-integer linear programming (MOMILP) model that considers population density and unemployment rate in the social dimension. The model is solved using the Lp-metric method and CPLEX solver. A scenario-based approach is used to address the uncertainty in demand and the return of worn-out tires. The results show to which extent considering social sustainability, along with uncertainties in demand and return, impacts location and technology selection decisions in the tire CLSC design problem. Besides, the economic and environmental dimensions are also affected when considering social indicators, because of relocation and changes in the distances between the various supply chain centers, which in turn results in changes in costs and pollutant emissions.

Keywords

References

References
Alinezhad, M., Mahdavi, I., Hematian, M., & Tirkolaee, E. B. (2022). A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries. Environment, Development and Sustainability, 1-28.
Akbari-Kasgari, M., Khademi-Zare, H., Fakhrzad, M. B., Hajiaghaei-Keshteli, M., & Honarvar, M. (2022). Designing a resilient and sustainable closed-loop supply chain network in copper industry. Clean Technologies and Environmental Policy, 24(5), 1553-1580.
Alizadeh, M., Ma, J., Marufuzzaman, M., & Yu, F. (2019). Sustainable olefin supply chain network design under seasonal feedstock supplies and uncertain carbon tax rate. Journal of cleaner production, 222, 280-299.
Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, 89, 369-384.
Amin, S. H., Zhang, G., & Akhtar, P. (2017). Effects of uncertainty on a tire closed-loop supply chain network. Expert Systems with Applications, 73, 82-91.
Azadeh, A., & Arani, H. V. (2016). Biodiesel supply chain optimization via a hybrid system dynamics-mathematical programming approach. Renewable energy, 93, 383-403.
Barbosa-Póvoa, A. P., da Silva, C., & Carvalho, A. (2018). Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research, 268(2), 399-431.
Chen, Z., & Andresen, S. (2014). A Multiobjective Optimization Model of Production‐Sourcing for Sustainable Supply Chain with Consideration of Social, Environmental, and Economic Factors. Mathematical Problems in Engineering, 2014(1), 616107.‏
Corti, A., & Lombardi, L. (2004). End life tires: Alternative final disposal processes compared by LCA. Energy, 29(12-15), 2089-2108.
Deliktaş, D., Karagoz, S., Simić, V., & Aydin, N. (2023). A stochastic Fermatean fuzzy-based multi-choice conic goal programming approach for sustainable supply chain management in end-of-life buildings. Journal of Cleaner Production, 382, 135305.
Dobrotă, D., Dobrotă, G., & Dobrescu, T. (2020). Improvement of waste tyre recycling technology based on a new tyre markings. Journal of Cleaner Production, 260, 121141.‏
Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega, 54, 11-32.
Fareeduddin, M., Hassan, A., Syed, M. N., & Selim, S. Z. (2015). The impact of carbon policies on closed-loop supply chain network design. Procedia CIRP, 26, 335-340.‏
Farrokh, M., Ahmadi, E., & Sun, M. (2023). A robust stochastic possibilistic programming model for dynamic supply chain network design with pricing and technology selection decisions. Opsearch, 60(3), 1082-1120.‏
Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2018). Multi-objective stochastic closed-loop supply chain network design with social considerations. Applied Soft Computing, 71, 505-525.
Feitó-Cespón, M., Sarache, W., Piedra-Jimenez, F., & Cespón-Castro, R. (2017). Redesign of a sustainable reverse supply chain under uncertainty: A case study. Journal of Cleaner Production, 151, 206-217.
Fontes, J., Tarne, P., Traverso, M., & Bernstein, P. (2018). Product social impact assessment. The International Journal of Life Cycle Assessment, 23, 547-555.‏
Goodarzian, F., Ghasemi, P., Gonzalez, E. D. S., & Tirkolaee, E. B. (2023). A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms. Journal of Environmental Management, 328, 116892.
Ghaderi, H., Moini, A., & Pishvaee, M. S. (2018). A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design. Journal of cleaner production, 179, 368-406.
Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28.
Hansen, U. E., Larsen, C., & Larsen, T. H. (2016). Outsourcing and Offshoring R&D in Green Technology to Emerging Economies: Opportunities and Challenges for Europe.
Kannan, D. (2018). Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. International Journal of Production Economics, 195, 391-418.
Khot, S. B., & Thiagarajan, S. (2024). Developing a resilient and sustainable non-linear closed-loop supply chain management framework for the automotive sector industry using a gaussian fuzzy optimization based non-linear model predictive control approach. Journal of Industrial and Production Engineering, 41(2), 101-120.‏
Kumar, L., Jain, P. K., & Sharma, A. K. (2020). A fuzzy goal programme–based sustainable Greenfield supply network design for tyre retreading industry. The International Journal of Advanced Manufacturing Technology, 108(9), 2855-2880.‏
Lebreton, B., & Tuma, A. (2006). A quantitative approach to assessing the profitability of car and truck tire remanufacturing. International Journal of production economics, 104(2), 639-652.
Lees, F. (2012). Lees' Loss prevention in the process industries: Hazard identification, assessment and control. Butterworth-Heinemann.
Lee, S. H., Park, S., & Kim, T. (2015). Review on investment direction of green technology R&D in Korea. Renewable and Sustainable Energy Reviews, 50, 186-193.
Mavi, R. K., Shekarabi, S. A. H., Mavi, N. K., Arisian, S., & Moghdani, R. (2023). Multi-objective optimisation of sustainable closed-loop supply chain networks in the tire industry. Engineering Applications of Artificial Intelligence, 126, 107116.‏
Martínez-Blanco, J., Lehmann, A., Muñoz, P., Antón, A., Traverso, M., Rieradevall, J., & Finkbeiner, M. (2014). Application challenges for the social Life Cycle Assessment of fertilizers within life cycle sustainability assessment. Journal of Cleaner Production, 69, 34-48.
Manupati, V. K., Kulatunga, A. K., Ray, P., & Rofin, T. M. (2024). Sustainable management of EOL tyres: A remanufacturing supply chain network design. Journal of Cleaner Production, 434, 140115.‏
Moghaddam, S. T., Javadi, M., & Molana, S. M. H. (2019). A reverse logistics chain mathematical model for a sustainable production system of perishable goods based on demand optimization. Journal of Industrial Engineering International, 15(4), 709-721.
Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2015). Towards supply chain sustainability: economic, environmental and social design and planning. Journal of cleaner production, 105, 14-27.‏
Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2018). Sustainable supply chains: An integrated modeling approach under uncertainty. Omega, 77, 32-57.
Osmani, A., & Zhang, J. (2017). Multi-period stochastic optimization of a sustainable multi-feedstock second generation bioethanol supply chain− A logistic case study in Midwestern United States. Land use policy, 61, 420-450.‏
Pasandideh, S. H. R., Niaki, S. T. A., & Asadi, K. (2015). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences, 292, 57-74.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2014). An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review, 67, 14-38.
Rao, C., Goh, M., Zhao, Y., & Zheng, J. (2015). Location selection of city logistics centers under sustainability. Transportation Research Part D: Transport and Environment, 36, 29-44.
Rohmer, S. U. K., Gerdessen, J. C., & Claassen, G. D. H. (2019). Sustainable supply chain design in the food system with dietary considerations: A multi-objective analysis. European Journal of Operational Research, 273(3), 1149-1164.
Roudi, F., Azadi, P., & Mesgaran, M. (2017). Iran’s population dynamics and demographic window of opportunity. Domestic Economy.
Seydanlou, P., Jolai, F., Tavakkoli-Moghaddam, R., & Fathollahi-Fard, A. M. (2022). A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: Hybrid meta-heuristic algorithms. Expert Systems with Applications, 203, 117566.
Sahebjamnia, N., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of cleaner production, 196, 273-296.
Santibañez-Aguilar, J. E., González-Campos, J. B., Ponce-Ortega, J. M., Serna-González, M., & El-Halwagi, M. M. (2014). Optimal planning and site selection for distributed multiproduct biorefineries involving economic, environmental and social objectives. Journal of cleaner production, 65, 270-294.‏
Shen, B., Zhu, C., Li, Q., & Wang, X. (2021). Green technology adoption in textiles and apparel supply chains with environmental taxes. International Journal of Production Research, 59(14), 4157-4174.‏
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(9), 2661-2702.
Taleizadeh, A. A., Haghighi, F., & Niaki, S. T. A. (2019). Modeling and solving a sustainable closed loop supply chain problem with pricing decisions and discounts on returned products. Journal of cleaner production, 207, 163-181.
Traverso, M., Finkbeiner, M., Jørgensen, A., & Schneider, L. (2012). Life cycle sustainability dashboard. Journal of industrial ecology, 16(5), 680-688.
Van Nguyen, T., Zhou, L., & Lin, Y. (2016, July). A multi-objective, multi-product and multi-transportation mode sustainable closed-loop supply chain network design. In 2016 International Conference on Logistics, Informatics and Service Sciences (LISS) (pp. 1-6). IEEE.‏
Yadollahinia, M., Teimoury, E., & Paydar, M. M. (2018). Tire forward and reverse supply chain design considering customer relationship management. Resources, Conservation and Recycling, 138, 215-228.
Yawar, S. A., & Seuring, S. (2019). 27 A framework for managing social issues in supply chains. Handbook on the Sustainable Supply Chain, 479.‏
Yue, D., Slivinsky, M., Sumpter, J., & You, F. (2014). Sustainable design and operation of cellulosic bioelectricity supply chain networks with life cycle economic, environmental, and social optimization. Industrial & Engineering Chemistry Research, 53(10), 4008-4029.
Yun, Y., Chuluunsukh, A., & Gen, M. (2020). Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach. Mathematics, 8(1), 84.
Zarei, M., Taghipour, H., & Hassanzadeh, Y. (2018). Survey of quantity and management condition of end-of-life tires in Iran: A case study in Tabriz. Journal of Material Cycles and Waste Management, 20(2), 1099-1105.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182-214.
Zhang, M., Tse, Y. K., Doherty, B., Li, S., & Akhtar, P. (2018). Sustainable supply chain management: Confirmation of a higher-order model. Resources, Conservation and Recycling, 128, 206-221.
International Journal of Supply and Operations Management

Articles in Press, Accepted Manuscript
Available Online from 17 May 2025

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