Utilizing Vehicular Ad Hoc Networks (VANET) for the Design of an Industrial Waste Reverse Supply Chain: A Case Study in the Iranian Automotive Industry

Document Type : Research Paper


1 Ph.D. Candidate, Department of Industrial Management, Alborz Campus, University of Tehran, Tehran, Iran.

2 Prof., Department of Industrial Management, Faculty of Industrial Management and Technology, School of Management, Tehran, Iran

3 Associate Prof., Department of Industrial Management, Faculty of Industrial Management and Technology, School of Management, Tehran, Iran.



In recent years, there has been a growing global concern regarding the escalating production of waste. As countries continue to industrialize, the challenge of managing and disposing of waste properly has become a daily issue. Consequently, the fate of industrial goods and products has become a topic of significant interest to consumers. Waste management operations and network design in the automotive industry differ in certain aspects from those in other industries. This difference basically comes from the complex structure of the supply chain in the automotive industry. A large number of sectors are involved in the supply chain, which makes it difficult to control and manage the reverse network. In addition, the high customization in cars means that the parts or components are not the same, and for this reason, it is difficult to predict the recycling of parts or materials. Also, the world is currently confronted not only with the challenges of environmental preservation and sustainability but also with rapid technological advancements in digitization and automation.This study aims to develop an industrial waste reverse supply chain network mathematical model (Iranian automotive industry) using the vehicular ad hoc network (VANET).
To maximize the economic benefits and minimize the environmental and social impacts, a multi-objective (multicriteria) mixed integer programming (MOMIP) facility location mathematical model was developed in the present study for a sustainable supply chain. The economic goal includes aspects such as income and costs within the supply chain, the environmental goal focuses on factors like carbon emissions during transportation and operations, and the social goal encompasses various elements such as annual accident rates, the well-being of drivers, the residential locations of the workforce, and workforce recruitment and termination. Also, to calculate more accurately, the amount of carbon emissions based on the duration of transportation in the supply chain used vehicular ad hoc network (VANET). Real data for the Iranian automotive industry and GAMS were used for model solution.
The proposed multi-objective mathematical model was solved using the enhanced epsilon constraint method in GAMS based on data obtained from the Iranian automotive industry. The results demonstrate the model's validation accuracy and indicate that the proposed model exhibits strong efficiency, making it well-suited for the Iranian case study.
The results showed that the reverse supply chain is efficient over time by considering the recycling of waste products at the same time as the economic, environmental, and social dimensions, as well as taking into account VANET. The model could determine the centers of dismantling facility, processing facility, and recycling according to the objectives of selection and the flow of materials between the centers. Sensitivity analysis also showed that changes to the parameter of culture making to advertise used car sales have a significant effect on the objective functions.


Main Subjects

Achillas, Ch., Vlachokostas, Ch., Aidonis, D., Moussiopoulos, N., Lakovou, E., and Banias, G. (2010). Optimising Reverse Logistics Network to Support Policy-Making in the Case of Electrical and Electronic Equipment. Waste Management, 30(12), 2592-2600.
Aieneh-Vand, S. & Gholamian, M.R. (2020). A location-inventory model of blood products (platelet) in the blood supply chain based on the EOQ ordering system. Industrial Management Journal, 12(4), 609-633. (in Persian)
Akbarpour Shirazi, M., Samieifard, R., Abduli, M.A., Omidvar, B. (2016). Mathematical modeling in municipal solid waste management: case study of Tehran. Journal of Environmental Health Science & Engineering, 14(1). DOI:10.1186/s40201-016-0250-2
Aminpour, S., Irajpour, A. & Yazdani, M. & Mohtashami, A. (2020). The Design of a Multi-directional Network Chain Model Offering a Closed Loop in the Automotive Industry by Providing Energy and Time Efficiency Programs. Industrial Management Journal, 12(1), 319-343. (in Persian)
Amritkar, M. (2017). Automatic Waste Management System with RFID and Ultrasonic Sensors. International Journal of Computer Sciences and Engineering, 5, 240-242. 10.26438/ijcse/v5i10.240242.
Aydın, N. (2020). Designing Reverse Logistics Network of End-Of-Life-Buildings as Preparedness to Disasters under Uncertainty. Journal of Cleaner Production, 256. 120341. 10.1016/j.jclepro.2020.120341.
Chakraborty, S. & Mehta, A. & Sheikh, Sh., Jha, A. & Manjunath, Cr. (2021). Smart waste management system. JETIR, 8(5).
Chan, F. T.S, Chan, H.K., Jain, V. (2012). A framework of reverse logistics for the automobile industry. International Journal of Production Research, 50(5), 1318-1331.
Damadi, H. & Namjoo, M.R. (2021). Smart Waste Management Using Blockchain. IT Professional, 23, 81-87. 10.1109/MITP.2021.3067710.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Doan, L.T.T., Amer, Y., Lee, S.-H., Phuc, P.N.K. & Dat, L.Q.  (2019). E-Waste Reverse Supply Chain: A Review and Future Perspectives. Applied Sciences, 9, 5195. DOI: 10.3390/app9235195.
 Eghbali, H., Arkat, J. & Tavakkoli-Moghaddam, R. )2022). Sustainable supply chain network design for municipal solid waste management: A case study. Journal of Cleaner Production, 381, Part 1. https://doi.org/10.1016/j.jclepro.2022.135211
Ene, S. & Öztürk, N. (2015). Network modeling for reverse flows of end-of-life vehicles. Waste Manag, http:// dx.doi.org/10.1016/j.wasman.2015.01.007.
Flores Castro, E. G. & Yoo, S.G. (2021). A Smart Waste Management System Based on LoRaWAN. International Conference on Futuristic Trends in Networks and Computing Technologies FTNCT 2020. Doi: 10.1007/978-981-16-1483-5_21.
Hogland, W. & Stenis, J. (2010). Assessment and system analysis of industrial waste management, Waste Management, vol.20, ppt.7-543
Jimenez, M., Arenas, M., & Bilbao, A. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
Khakim Habibia, M.K., Battaïab, O., Cungc, V-D., Dolgui, A. (2017). Collection–Disassembly Problem in Reverse Supply Chain. International Journal of Production Economics, 183, Part B., 334- 344.
Kuroki, H. & Ishigaki, A. & Takashima, R. (2020). A location-routing problem with economic efficiency for recycling system. Procedia Manufacturing. 43. 215-222. 10.1016/j.promfg.2020.02.139.
Kuşakc, A.O., Ayvaz, B., Cin, E., Aydın, N. (2019). Optimization of reverse logistics network of End of Life Vehicles under fuzzy supply: A case study for Istanbul Metropolitan Area, Journal of Cleaner Production. doi: https://doi.org/10.1016/j.jclepro.2019.01.090.
Lin, Y., Jia, H., Yang, Y., Tian, G., Tao, F., Ling, L. (2018). An improved artificial bee colony for facility location allocation problem of end-of-life vehicles recovery network. Journal of Cleaner Production, 205, 134-144.
Mahmoudzadeh, M., Mansour, S., Karimi, B. (2011). A Decentralized Reverse Logistics Network for End of Life Vehicles from Third Party Provider Perspective. 2nd International Conference on Environmental Science and Technology.
Marr, B. (2016). Why Everyone Must Get Ready For The 4th Industrial Revolution. Forbes (blog). Retrieved 2016-12-12.
Mavrotas, G., Florios, K. (2013). An improved version of the augmented e-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669.
Mirjalili, S.A. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95. 51-67. DOI: 10.1016/j.advengsoft.2016.01.008.
Momeni, M. & Zereshki, N. (2021). Modeling of Closed-Loop Supply Chains by Utilizing Scenario-Based Approaches in Facing Uncertainty in Quality and Quantity of Returns. Industrial Management Journal, 13(1), 105-130. (in Persian)
Mosallanezhad, B., Gholian-Jouybari, F., Cárdenas-Barrón, L.E. & Hajiaghaei-Keshteli, M. (2023). The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW). Engineering Applications of Artificial Intelligence, 120. https://doi.org/10.1016/j.engappai.2023.105903
Olapiriyakul, S. & Pannakkong, W. & Kachapanya, W. & Starita, S. (2019). Multiobjective Optimization Model for Sustainable Waste Management Network Design. Journal of Advanced Transportation,1-15. Doi: 10.1155/2019/3612809.
Rabbani, M., Mokhtarzadeh, M., Farrokhi-Asl, H. (2018). A New Mathematical Model for Designing a Municipal Solid Waste System Considering Environmentally Issues. Int J Supply Oper Manage (IJSOM), 5(3).
Roshan, R. & Rishi, O.P. (2020). Effective and Efficient Smart Waste Management System for the Smart Cities Using Internet of Things (IoT): An Indian Perspective. Conference Paper, DOI: 10.1007/978-981-15-6014-9_54.
Safdar, N. & Khalid, R. & Ahmed, W. & Imran, M. (2020). Reverse logistics network design of e-waste management under the triple bottom line approach. Journal of Cleaner Production, 272. Doi: 10.1016/j.jclepro.2020.122662.
Sajadieh, M. & Shadrokh, Sh. & Hassanzadeh, F. (2009). Concurrent Project Scheduling and Material Planning: A Genetic Algorithm Approach. Industrial Engineering, 16(2), 91- 99.
Sasikumar, P., Kannan, G., and Noorul Haq, A. N. (2010). A Multi-Echelon Reverse Logistics Network Design for Product Recovery – A Case of Truck Tire Remanufacturing. International Journal of Advanced Manufacturing Technology, 49(9-12), 1223-1234.
Shyam, G. & Manvi, S.S. & Priyanka, B. (2017). Smart waste management using Internet-of-Things (IoT). 2nd International Conference on Computing and Communications Technologies (ICCCT). 199-203. DOI: 10.1109/ICCCT2.2017.7972276.
Singh, T. & Mahajan, R. & Bagai, D. (2016). Smart Waste Management using Wireless Sensor Network. International Journal of Innovative Research in Computer and Communication Engineering, 4(6), 1111-1115. DOI: 10.15680/IJIRCCE.2016. 0405001 
Tavakkoli-Moghaddam, R. & Azarkish, M. & Sadeghnejad, A. (2011). A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Expert Systems with Applications, 38(9), 10812-10821. Doi: 10.1016/j.eswa.2011.02.050.
Vijay, Sh. & Kumar, P. & Raju, S. (2019). Smart Waste Management System using ARDUINO. 45th Series Student Project Programme (SPP) – 2021-22.
Wang, X., Zhao, M., He, H. (2018). Reverse Logistic Network Optimization Research for Sharing Bikes. Procedia Computer Science, 126, 1693–1703.
Xiao, ZH., Suna, J.B., Shua, W., Wanga, T. (2019). Location-allocation problem of reverse logistics for end-of-life vehicles based on the measurement of carbon emissions. Computers & Industrial Engineering, 127, 169-181.
Xie, C., Deng, X., Zhang, J., Wang, Y., Zheng, L., Ding, X., Li, X. & Wu, L. (2023). Multi-period design and optimization of classified municipal solid waste supply chain integrating seasonal fluctuations in waste generation. Sustainable Cities and Society, 93. https://doi.org/10.1016/j.scs.2023.104522
Xu, Z., Elomri, A., Pokharel, Sh., Zhang, Q., Ming, X. & Liu, W. (2017). Global reverse supply chain design for solid waste recycling under uncertainties and carbon emission constraint. Waste Management. 64, 358-370. DOI: 10.1016/j.wasman.2017.02.024.
Yu, H., Solvang, W-D. (2016). A general reverse logistics network design model for product reused and recycling with environmental considerations. The International Journal of Advanced Manufacturing Technology, 87, 2693.