Fuzzy Robust Optimization in Closed-Loop Supply Chain Network Model for Hazardous Products (Lead-Acid Battery)

Authors

  • Danial Rashidi Meybodi * Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
  • Hamed Tayebi Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
  • Sina Laleh Department of Industrial Engineering, Tehran West Branch, Islamic Azad University, Tehran, Iran

DOI:

https://doi.org/10.52547/ijimes.2.2.61

DOR:

https://dorl.net/dor/20.1001.1.27832678.2022.2.2.6.5

Keywords:

Closed Loop Supply Chain, Fuzzy Robust Optimization, Hazardous Products, Lead Acid Battery

Abstract

Purpose: This paper models a closed-loop supply chain network problem for hazardous products in the face of demand uncertainty and variable costs. The designed model includes a set of suppliers, production centers, distribution, recycling, disposal, collection and end customers in which strategic and tactical decisions are made simultaneously. Among the decisions made in this paper is the location of production, distribution and collection centers and determining the optimal amount of product flow between the levels of the supply chain network.

Methodology: In this paper, the Epsilon constraint method is used to solve a multi-objective model in GMAS software. This article also uses uniform data to solve the problem.

Findings: The results of solving the model with fuzzy robust optimization method show that with increasing the uncertainty rate and also reducing the transfer time of hazardous products, the total network costs as well as the amount of greenhouse gas emissions have increased. Also, the study of Pareto front to optimize the total design costs and the amount of greenhouse gas emissions shows that by reducing the amount of greenhouse gas emissions in the network, the costs related to location and routing increase.

Originality/Value: In this paper a fuzzy robust optimization is used in closed-loop supply chain network model for hazardous products (Lead-Acid Battery).

Downloads

Download data is not yet available.

References

Court, C. D., Munday, M., Roberts, A., & Turner, K. (2015). Can hazardous waste supply chain ‘hotspots’ be identified using an input–output framework?. European Journal of Operational Research, 241(1), 177-187. https://doi.org/10.1016/j.ejor.2014.08.011

Mohri, S. S., Mohammadi, M., Gendreau, M., Pirayesh, A., Ghasemaghaei, A., & Salehi, V. (2021). Hazardous material transportation problems: A comprehensive overview of models and solution approaches. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2021.11.045

Hu, H., Du, J., Li, X., Shang, C., & Shen, Q. (2020). Risk models for hazardous material transportation subject to weight variation considerations. IEEE Transactions on Fuzzy Systems, 29(8), 2271-2282. DOI: 10.1109/TFUZZ.2020.2997467

Ma, H., Li, X., & Liu, Y. (2020). Multi-period multi-scenario optimal design for closed-loop supply chain network of hazardous products with consideration of facility expansion. Soft Computing, 24(4), 2769-2780. https://doi.org/10.1007/s00500-019-04435-z

Nozari, H., Ghahremani-Nahr, J., & Szmelter-Jarosz, A. (2022). A multi-stage stochastic inventory management model for transport companies including several different transport modes. International Journal of Management Science and Engineering Management, 1-11. https://doi.org/10.1080/17509653.2022.2042747

Ghahremani Nahr, J., Kian, R., & Rezazadeh, H. (2018). A modified priority-based encoding for design of a closed-loop supply chain network using a discrete league championship algorithm. Mathematical problems in engineering, 2018. https://doi.org/10.1155/2018/8163927

Yang, Q., Chin, K. S., & Li, Y. L. (2018). A quality function deployment-based framework for the risk management of hazardous material transportation process. Journal of Loss Prevention in the Process Industries, 52, 81-92. https://doi.org/10.1016/j.jlp.2018.02.001

Degla, A., Chikh, M., Chouder, A., Bouchafaa, F., & Taallah, A. (2018). Update battery model for photovoltaic application based on comparative analysis and parameter identification of lead–acid battery models behaviour. IET Renewable Power Generation, 12(4), 484-493.

Szmelter-Jarosz, A., Ghahremani-Nahr, J., & Nozari, H. (2021). A neutrosophic fuzzy optimisation model for optimal sustainable closed-loop supply chain network during COVID-19. Journal of Risk and Financial Management, 14(11), 519. https://doi.org/10.3390/jrfm14110519

Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015). A fuzzy goal programming model to strategic planning problem of a lead/acid battery closed-loop supply chain. Journal of Manufacturing Systems, 37, 243-264. https://doi.org/10.1016/j.jmsy.2014.09.001

Ko, H. J., & Evans, G. W. (2007). A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research, 34(2), 346-366. https://doi.org/10.1016/j.cor.2005.03.004

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. https://doi.org/10.1016/j.apm.2009.06.021

Sasikumar, P., & Haq, A. N. (2011). Integration of closed loop distribution supply chain network and 3PRLP selection for the case of battery recycling. International Journal of Production Research, 49(11), 3363-3385. https://doi.org/10.1080/00207541003794876

Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied mathematical modelling, 35(2), 637-649. https://doi.org/10.1016/j.apm.2010.07.013

Carle, M. A., Martel, A., & Zufferey, N. (2012). The CAT metaheuristic for the solution of multi-period activity-based supply chain network design problems. International Journal of Production Economics, 139(2), 664-677. https://doi.org/10.1016/j.ijpe.2012.06.016

Pazhani, S., Ramkumar, N., Narendran, T. T., & Ganesh, K. (2013). A bi-objective network design model for multi-period, multi-product closed-loop supply chain. Journal of industrial and production engineering, 30(4), 264-280. https://doi.org/10.1080/21681015.2013.830648

Hatefi, S. M., & Jolai, F. (2014). Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. Applied mathematical modelling, 38(9-10), 2630-2647. https://doi.org/10.1016/j.apm.2013.11.002

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. https://doi.org/10.1016/j.tre.2013.11.001

Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, 70, 225-244. https://doi.org/10.1016/j.tre.2014.06.003

Zeballos, L. J., Méndez, C. A., & Barbosa-Povoa, A. P. (2016). Design and planning of closed-loop supply chains: a risk-averse multistage stochastic approach. Industrial & Engineering Chemistry Research, 55(21), 6236-6249. https://doi.org/10.1021/acs.iecr.5b03647

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. https://doi.org/10.1016/j.tre.2016.02.011

Zhang, Z. H., & Unnikrishnan, A. (2016). A coordinated location-inventory problem in closed-loop supply chain. Transportation Research Part B: Methodological, 89, 127-148. https://doi.org/10.1016/j.trb.2016.04.006

Hafezalkotob, A., Khalili-Damghani, K., & Ghashami, S. S. (2016). A three-echelon multi-objective multi-period multi-product supply chain network design problem: A goal programming approach. Journal of Optimization in Industrial Engineering, 10(21), 67-78. DOI: 10.22094/joie.2016.262

Ma, H., & Li, X. (2018). Closed-loop supply chain network design for hazardous products with uncertain demands and returns. Applied Soft Computing, 68, 889-899. https://doi.org/10.1016/j.asoc.2017.10.027

Mohammed, F., Selim, S. Z., Hassan, A., & Syed, M. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment, 51, 146-172. https://doi.org/10.1016/j.trd.2016.10.033

Nayeri, S., Paydar, M. M., Asadi-Gangraj, E., & Emami, S. (2020). Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design. Computers & Industrial Engineering, 148, 106716. https://doi.org/10.1016/j.cie.2020.106716

Liu, Z., Wu, Y., Liu, T., Wang, X., Li, W., Yin, Y., & Xiao, X. (2021). Double Path Optimization of Transport of Industrial Hazardous Waste Based on Green Supply Chain Management. Sustainability, 13(9), 5215. https://doi.org/10.3390/su13095215

Takhar, S. S., & Liyanage, K. (2021). Transforming product labels using digital technologies to enable enhanced traceability and management of hazardous chemicals. International Journal of Supply Chain and Operations Resilience, 5(1), 27-59.

Mohabbati-Kalejahi, N., & Vinel, A. (2021). Robust Hazardous Materials Closed-Loop Supply Chain Network Design with Emergency Response Teams Location. Transportation Research Record, 2675(6), 306-329. https://doi.org/10.1177/0361198121992071

Ke, G. Y. (2022). Managing reliable emergency logistics for hazardous materials: A two-stage robust optimization approach. Computers & Operations Research, 138, 105557. https://doi.org/10.1016/j.cor.2021.105557

Zarei, E., Gholamizadeh, K., Khan, F., & Khakzad, N. (2022). A dynamic domino effect risk analysis model for rail transport of hazardous material. Journal of Loss Prevention in the Process Industries, 74, 104666. https://doi.org/10.1016/j.jlp.2021.104666.

Downloads

Published

2022-04-23

How to Cite

Rashidi Meybodi, D., Tayebi, H., & Laleh, S. (2022). Fuzzy Robust Optimization in Closed-Loop Supply Chain Network Model for Hazardous Products (Lead-Acid Battery). International Journal of Innovation in Management, Economics and Social Sciences, 2(2), 61–82. https://doi.org/10.52547/ijimes.2.2.61

Issue

Section

Original Research