Designing an Optimal Recycling Model in a Four-Level Closed-Loop Supply Chain by Queuing Theory and Robust Planning
(Case Study: Paper Industry)
Subject Areas :
Industrial Management
Mahdi Alizadeh Beromi
1
,
Mohammad Ali Afshar Kazemi
2
,
Mohammadali Keramati
3
,
Abbass Toloie Ashlaghi
4
1 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
Received: 2023-08-10
Accepted : 2023-10-16
Published : 2023-09-23
Keywords:
Closed-loop supply chain,
robust programming,
mixed linear programming,
Multi-objective meta-heuristic algorithms,
Queuing theory,
Abstract :
In recent years, the growing industrial and economic competition, environmental concerns, and governmental pressures on manufacturers regarding waste management have underscored the significance of designing a reverse supply chain and closed-loop network. Simultaneously, the potential for profit arising from product recycling has further emphasized the importance of these systems. This research focuses on developing a four-stage closed-loop network model for the supply chain, taking into account the uncertainty of product recycling rates. The primary objective of this study is to provide an integer linear programming model aimed at minimizing supply chain costs and customer service time under uncertain conditions. The supply chain model is designed by integrating queuing theory and product recycling system optimization. A critical aspect of this research involves modeling the uncertainty in the return rate of consumer products in the closed-loop supply chain, with the aim of developing a robust approach to address this issue. Additionally, the performance of the proposed model in the paper industry is evaluated, and a sensitivity analysis is conducted with respect to the decision variables using two metaheuristic algorithms: the Multiple Objective Harmony Search and the Non-dominated Sorting Genetic Algorithm.
References:
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Alinezhad, A., Hajipour, V., & Mahmoudi, A. (2015). Optimizing Queuing-Inventory Problems under Uncertainty: A Fuzzy Mathematical Programming. Journal of Industrial Mnagemnet, 27(9), 17-32.
Alinezhad, A., kazemi, A., & Karimi, M. (2020). A Multi-Objective Model for Location-Routing Problem Considering Minimal Risk and Maximal Demand Covering. Industrial Management Studies, 18(58), 105-138. doi: 10.22054/jims.2020.36793.2184
Asim, Z., Jalil, S. A., & Javaid, S. (2019). An uncertain model for integrated production-transportation closed-loop supply chain network with cost reliability. Sustainable Production and Consumption, 17, 298-310.
Askin, R. G., & Hanumantha, G. J. (2018). Queueing network models for analysis of nonstationary manufacturing systems. International Journal of Production Research, 56(1-2), 22-42.
Bundschuh, J., Kaczmarczyk, M., Ghaffour, N., & Tomaszewska, B. (2021). State-of-the-art of renewable energy sources used in water desalination: Present and future prospects. Desalination, 508, 115035.
Baghizadeh, K., Cheikhrouhou, N., Govindan, K., & Ziyarati, M. (2022). Sustainable agriculture supply chain network design considering water‐energy‐food nexus using queuing system: A hybrid robust possibilistic programming. Natural Resource Modeling, 35(1), e12337.
Bathaee, M., Nozari, H., & Szmelter-Jarosz, A. (2023). Designing a new location-allocation and routing model with simultaneous pick-up and delivery in a closed-loop supply chain network under uncertainty. Logistics, 7(1), 3.
Chang, H. C., Ouyang, L. Y., Wu, K. S., & Ho, C. H. (2006). Integrated vendor–buyer cooperative inventory models with controllable lead time and ordering cost reduction. European Journal of Operational Research, 170(2), 481-495.
Chin, T. L., Chen, Y. S., & Lyu, K. Y. (2020). Queuing model based edge placement for work offloading in mobile cloud networks. IEEE Access, 8, 47295-47303.
Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European journal of operational research, 235(3), 594-615.
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Ding, J., Chen, X., Sun, H., Yan, W., & Fang, H. (2021). Hierarchical structure of a green supply chain. Computers & Industrial Engineering, 157, 107303.
Jang, Y. C., Lee, G., Kwon, Y., Lim, J. H., & Jeong, J. H. (2020). Recycling and management practices of plastic packaging waste towards a circular economy in South Korea. Resources, Conservation and Recycling, 158, 104798.
Jouyban, F., yousefi, M., & Neyshaboori, E. (2018). Presenting a bi objective stochastic pharmaceutical supply chain model considering time and cost. Journal of Industrial Mnagemnet, 13(44), 15-28.
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Mohammadpour, H., & Alinezhad, A. (2015). A mathematical programming model for location-routing problem and solve it using an efficient meta-heuristic method. Appl. Environ. Biol. Sci, 5(12S), 579-594.
Pishvaee, 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.
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Shahtaheri, Y., Flint, M. M., & de la Garza, J. M. (2019). A multi-objective reliability-based decision support system for incorporating decision maker utilities in the design of infrastructure. Advanced Engineering Informatics, 42, 100939.
Soon, A., Heidari, A., Khalilzadeh, M., Antucheviciene, J., Zavadskas, E. K., & Zahedi, F. (2022). Multi-objective sustainable closed-loop supply chain network design considering multiple products with different quality levels. Systems, 10(4), 94.
Valizadeh, J., Sadeh, E., Sabegh, Z. A., & Hafezalkotob, A. (2020). Robust optimization model for sustainable supply chain for production and distribution of polyethylene pipe. Journal of Modelling in Management, 15(4), 1613-1653.
Viswanadham, N., & Raghavan, N. S. (2001, May). Performance modeling of supply chains using queueing networks. In Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164)(Vol. 1, pp. 529-534). IEEE.
Wierzbicka, A. (2020). Queue theory and improving the customer service process in the city hall–case study. Organizacja i Zarządzanie: kwartalnik naukowy.
Zhang, H. Y., Xi, S. H., Chen, Q. X., Smith, J. M., Mao, N., & Li, X. (2021). Performance analysis of a flexible flow shop with random and state-dependent batch transport. International Journal of Production Research, 59(4), 982-1002.
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Aliahmadi, A., Ghahremani-Nahr, J., & Nozari, H. (2023). Pricing decisions in the closed-loop supply chain network, taking into account the queuing system in production centers. Expert Systems with Applications, 212, 118741.
Alinezhad, A., Hajipour, V., & Mahmoudi, A. (2015). Optimizing Queuing-Inventory Problems under Uncertainty: A Fuzzy Mathematical Programming. Journal of Industrial Mnagemnet, 27(9), 17-32.
Alinezhad, A., kazemi, A., & Karimi, M. (2020). A Multi-Objective Model for Location-Routing Problem Considering Minimal Risk and Maximal Demand Covering. Industrial Management Studies, 18(58), 105-138. doi: 10.22054/jims.2020.36793.2184
Asim, Z., Jalil, S. A., & Javaid, S. (2019). An uncertain model for integrated production-transportation closed-loop supply chain network with cost reliability. Sustainable Production and Consumption, 17, 298-310.
Askin, R. G., & Hanumantha, G. J. (2018). Queueing network models for analysis of nonstationary manufacturing systems. International Journal of Production Research, 56(1-2), 22-42.
Bundschuh, J., Kaczmarczyk, M., Ghaffour, N., & Tomaszewska, B. (2021). State-of-the-art of renewable energy sources used in water desalination: Present and future prospects. Desalination, 508, 115035.
Baghizadeh, K., Cheikhrouhou, N., Govindan, K., & Ziyarati, M. (2022). Sustainable agriculture supply chain network design considering water‐energy‐food nexus using queuing system: A hybrid robust possibilistic programming. Natural Resource Modeling, 35(1), e12337.
Bathaee, M., Nozari, H., & Szmelter-Jarosz, A. (2023). Designing a new location-allocation and routing model with simultaneous pick-up and delivery in a closed-loop supply chain network under uncertainty. Logistics, 7(1), 3.
Chang, H. C., Ouyang, L. Y., Wu, K. S., & Ho, C. H. (2006). Integrated vendor–buyer cooperative inventory models with controllable lead time and ordering cost reduction. European Journal of Operational Research, 170(2), 481-495.
Chin, T. L., Chen, Y. S., & Lyu, K. Y. (2020). Queuing model based edge placement for work offloading in mobile cloud networks. IEEE Access, 8, 47295-47303.
Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European journal of operational research, 235(3), 594-615.
Dzwigol, H., Dzwigol-Barosz, M., & Kwilinski, A. (2020). Formation of global competitive enterprise environment based on industry 4.0 concept. International Journal of Entrepreneurship, 24(1), 1-5.
Ding, J., Chen, X., Sun, H., Yan, W., & Fang, H. (2021). Hierarchical structure of a green supply chain. Computers & Industrial Engineering, 157, 107303.
Jang, Y. C., Lee, G., Kwon, Y., Lim, J. H., & Jeong, J. H. (2020). Recycling and management practices of plastic packaging waste towards a circular economy in South Korea. Resources, Conservation and Recycling, 158, 104798.
Jouyban, F., yousefi, M., & Neyshaboori, E. (2018). Presenting a bi objective stochastic pharmaceutical supply chain model considering time and cost. Journal of Industrial Mnagemnet, 13(44), 15-28.
Laili, Y., Wang, Y., Fang, Y., & Pham, D. T. (2022). Optimisation of robotic disassembly for remanufacturing. Springer.
Mahmoodi, F., & Pouyan far, F. (2020). Drug logistics network design based on the fleet routing by using the improved gray wolf optimizer algorithm. Journal of Industrial Mnagemnet, 15(53), 96-114
Marić, J., & Opazo-Basáez, M. (2019). Green servitization for flexible and sustainable supply chain operations: A review of reverse logistics services in manufacturing. Global Journal of Flexible Systems Management, 20, 65-80.
Mohtashami, Z., Aghsami, A., & Jolai, F. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of cleaner production, 242, 118452.
Mohammadpour, H., & Alinezhad, A. (2015). A mathematical programming model for location-routing problem and solve it using an efficient meta-heuristic method. Appl. Environ. Biol. Sci, 5(12S), 579-594.
Pishvaee, 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.
Rezaee Nik, E., & Asadi Zeidabadi, S. (2023). Comparison of NSGA-II and SPEA2 algorithms in a bi-objective robust scenario-based supply chain considering material waste. Sharif Journal of Industrial Engineering & Management, (), -. doi: 10.24200/j65.2022.58984.2251.
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
Shahtaheri, Y., Flint, M. M., & de la Garza, J. M. (2019). A multi-objective reliability-based decision support system for incorporating decision maker utilities in the design of infrastructure. Advanced Engineering Informatics, 42, 100939.
Soon, A., Heidari, A., Khalilzadeh, M., Antucheviciene, J., Zavadskas, E. K., & Zahedi, F. (2022). Multi-objective sustainable closed-loop supply chain network design considering multiple products with different quality levels. Systems, 10(4), 94.
Valizadeh, J., Sadeh, E., Sabegh, Z. A., & Hafezalkotob, A. (2020). Robust optimization model for sustainable supply chain for production and distribution of polyethylene pipe. Journal of Modelling in Management, 15(4), 1613-1653.
Viswanadham, N., & Raghavan, N. S. (2001, May). Performance modeling of supply chains using queueing networks. In Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164)(Vol. 1, pp. 529-534). IEEE.
Wierzbicka, A. (2020). Queue theory and improving the customer service process in the city hall–case study. Organizacja i Zarządzanie: kwartalnik naukowy.
Zhang, H. Y., Xi, S. H., Chen, Q. X., Smith, J. M., Mao, N., & Li, X. (2021). Performance analysis of a flexible flow shop with random and state-dependent batch transport. International Journal of Production Research, 59(4), 982-1002.
