A Decision-Making Approach Based on Time Series to Configure a Responsive and Sustainable Supply Chain Considering Different Transportation Modes under Uncertainty
Behzad Shahram
1
(
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran
)
Ali Naderan
2
(
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran
)
Hassan Javanshir
3
(
Department of Industrial Engineering, Faculty of Industry,South Tehran Branch, Islamic Azad University, Tehran, Iran.
)
الکلمات المفتاحية: Sustainable supply chain, Data-driven decision-making, Transportation modes, Responsive supply chain, Medical equipment,
ملخص المقالة :
The importance of logistics activities in the nowadays modern and competitive marketplace has attracted the attention of researchers toward the supply chain management (SCM) problem. Also, by increasing environmental and social concerns, the concept of sustainability has been drastically highlighted in today’s logistics networks. Therefore, the current study focuses on designing a closed-loop supply chain based on the sustainability and responsiveness dimensions by considering different transportation modes using a data-driven model. In this research, at the outset, a mathematical model is proposed to optimize the sustainability dimensions by considering the responsiveness metric. Then, since the dynamic nature of the business environment leads to the high level of uncertainty, we combine the SARIMA (Seasonal Autoregressive Integrated Moving Average) and the Possibilistic Robust Stochastic (PRS) methods to develop a data-driven framework for tackling the mixed uncertainty. Afterward, by considering the medical equipment industry as a real-world application, a heuristic-based meta-goal programming (MGP) method is developed to solve the proposed model. The achieved results show the appropriate performance of the proposed data-driven model. Afterward, a series of sensitivity analyses are performed to evaluate how key model parameters influence the research problem, leading to the generation of relevant managerial insights.
Abbasi, S., Daneshmand-Mehr, M., & Ghane Kanafi, A. (2023). Green closed-loop supply chain network design during the coronavirus (COVID-19) pandemic: A case study in the Iranian Automotive Industry. Environmental Modeling & Assessment, 28(1), 69–103.
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.
Alizadeh, Z., Otero, B. M., & Jalilzadeh, A. (2022). An Inexact Variance-Reduced Method For Stochastic Quasi-Variational Inequality Problems With An Application In Healthcare. arXiv preprint arXiv:2209.00087.
Arabi, M., & Gholamian, M. R. (n.d.). Resilient Closed-Loop Supply Chain Network Design Considering Quality Uncertainty: A Case Study of Stone Quarries. Available at SSRN 4007120.
Asadi, Z., Khatir, M. V., & Rahimi, M. (2022). Robust design of a green-responsive closed-loop supply chain network for the ventilator device. Environmental Science and Pollution Research, 1–21.
Babaeinesami, A., Tohidi, H., & Seyedaliakbar, S. M. (2021). Designing a data-driven leagile sustainable closed-loop supply chain network. International Journal of Management Science and Engineering Management, 16(1), 14–26.
Bai, C., Zhu, Q., & Sarkis, J. (2024). Circular economy and circularity supplier selection: a fuzzy group decision approach. International Journal of Production Research, 62(7), 2307–2330.
Bairamzadeh, S., Saidi-Mehrabad, M., & Pishvaee, M. S. (2018). Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach. Renewable energy, 116, 500–517.
Bertsimas, D., Gupta, V., & Kallus, N. (2018). Data-driven robust optimization. Mathematical Programming, 167(2), 235–292.
Boroun, M., Alizadeh, Z., & Jalilzadeh, A. (2023). Accelerated Primal-dual Scheme for a Class of Stochastic Nonconvex-concave Saddle Point Problems. arXiv preprint arXiv:2303.00211.
Fazli-Khalaf, M., Mirzazadeh, A., & Pishvaee, M. S. (2017). A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network. Human and Ecological Risk Assessment: An International Journal, 23(8), 2119–2149.
Fazli-Khalaf, M., Naderi, B., Mohammadi, M., & Pishvaee, M. S. (2021). The design of a resilient and sustainable maximal covering closed-loop supply chain network under hybrid uncertainties: a case study in tire industry. Environment, Development and Sustainability, 23(7), 9949–9973.
ForouzeshNejad, A. A. (2022). Leagile and sustainable supplier selection problem in the Industry 4.0 era: a case study of the medical devices using hybrid multi-criteria decision making tool. Environmental Science and Pollution Research, 1–20.
Gao, Y., Lu, S., Cheng, H., & Liu, X. (2024). Data-driven robust optimization of dual-channel closed-loop supply chain network design considering uncertain demand and carbon cap-and-trade policy. Computers & Industrial Engineering, 187, 109811.
Ghalandari, M., Amirkhan, M., & Amoozad-Khalili, H. (2023). A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry. Environmental Science and Pollution Research, 30(1), 451–476.
Gholizadeh, H., Fazlollahtabar, H., & Khalilzadeh, M. (2020). A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data. Journal of Cleaner Production, 120640.
Gholizadeh, H., Jahani, H., Abareshi, A., & Goh, M. (2021). Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization. Computers & Industrial Engineering, 157, 107324.
Jamali, A., Ranjbar, A., Heydari, J., & Nayeri, S. (2021). A multi-objective stochastic programming model to configure a sustainable humanitarian logistics considering deprivation cost and patient severity. Annals of Operations Research, 1–36.
Kaur, H., & Singh, S. P. (2018). Heuristic modeling for sustainable procurement and logistics in a supply chain using big data. Computers & Operations Research, 98, 301–321.
Lee, J., Ko, C., & Moon, I. (2024). E-commerce supply chain network design using on-demand warehousing system under uncertainty. International Journal of Production Research, 62(5), 1901–1927.
Li, G., Lu, M., Lai, S., & Li, Y. (2023). Research on Power Battery Recycling in the Green Closed-Loop Supply Chain: An Evolutionary Game-Theoretic Analysis. Sustainability, 15(13), 10425.
Mamashli, Z., Bozorgi-Amiri, A., Dadashpour, I., Nayeri, S., & Heydari, J. (2021). A heuristic-based multi-choice goal programming for the stochastic sustainable-resilient routing-allocation problem in relief logistics. Neural Computing and Applications, 1–27.
Mamashli, Z., Nayeri, S., Tavakkoli-Moghaddam, R., Sazvar, Z., & Javadian, N. (2021). Designing a sustainable–resilient disaster waste management system under hybrid uncertainty: A case study. Engineering Applications of Artificial Intelligence, 106, 104459.
Najafi, M., & Zolfagharinia, H. (2024). A Multi-objective integrated approach to address sustainability in a meat supply chain. Omega, 124, 103011.
Nasrollah, S., Najafi, S. E., Bagherzadeh, H., & Rostamy-Malkhalifeh, M. (2022). An enhanced PSO algorithm to configure a responsive-resilient supply chain network considering environmental issues: a case study of the oxygen concentrator device. Neural Computing and Applications, 1–32.
Nayeri, S., Asadi-Gangraj, E., & Emami, S. (2018). Goal programming-based post-disaster decision making for allocation and scheduling the rescue units in natural disaster with time-window. International Journal of Industrial Engineering & Production Research, 29(1), 65–78.
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, 106716.
Nayeri, S., Sazvar, Z., & Heydari, J. (2022). A global-responsive supply chain considering sustainability and resiliency: Application in the medical devices industry. Socio-Economic Planning Sciences, 101303.
Nayeri, S., Tavakkoli-Moghaddam, R., Sazvar, Z., & Heydari, J. (2021). A heuristic-based simulated annealing algorithm for the scheduling of relief teams in natural disasters. Soft Computing, 1–19.
Nayeri, S., Tavakoli, M., Tanhaeean, M., & Jolai, F. (2021). A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms. Annals of Operations Research, 1–41.
Nayeri, S., Torabi, S. A., Tavakoli, M., & Sazvar, Z. (2021). A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network. Journal of Cleaner Production, 127691.
Pahlevan, S. M., Hosseini, S. M. S., & Goli, A. (2021). Sustainable supply chain network design using products’ life cycle in the aluminum industry. Environmental Science and Pollution Research, 1–25.
Rabbani, M., Sabbaghnia, A., Mobini, M., & Razmi, J. (2018). A graph theory-based algorithm for a multi-echelon multi-period responsive supply chain network design with lateral-transshipments. Operational Research, 1–21.
Rahmanian, V., Bokaie, S., Haghdoost, A., & Barouni, M. (2021). Predicting cutaneous leishmaniasis using SARIMA and Markov switching models in Isfahan, Iran: A time-series study. Asian Pacific Journal of Tropical Medicine, 14(2), 83. https://doi.org/10.4103/1995-7645.306739
Rostami, O., Tavakoli, M., Tajally, A., & GhanavatiNejad, M. (2023). A goal programming-based fuzzy best–worst method for the viable supplier selection problem: a case study. Soft Computing, 27(6), 2827–2852.
Saisridhar, P., Thürer, M., & Avittathur, B. (2024). Assessing supply chain responsiveness, resilience and robustness (Triple-R) by computer simulation: a systematic review of the literature. International Journal of Production Research, 62(4), 1458–1488.
Sazvar, Z., Tafakkori, K., Oladzad, N., & Nayeri, S. (2021). A Capacity Planning Approach for Sustainable-Resilient Supply Chain Network Design under Uncertainty: A Case Study of Vaccine Supply Chain. Computers & Industrial Engineering, 107406.
Sazvar, Z., Tavakoli, M., Ghanavati-Nejad, M., & Nayeri, S. (2022). Sustainable-resilient supplier evaluation for high-consumption drugs during COVID-19 pandemic using a data-driven decision-making approach. Scientia Iranica.
Sazvar, Z., Zokaee, M., Tavakkoli-Moghaddam, R., Salari, S. A., & Nayeri, S. (2021). Designing a sustainable closed-loop pharmaceutical supply chain in a competitive market considering demand uncertainty, manufacturer’s brand and waste management. Annals of Operations Research, 1–32.
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.
Sheykhzadeh, M., Ghasemi, R., Vandchali, H. R., Sepehri, A., & Torabi, S. A. (2024). A hybrid decision-making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: a case study of a pharmaceutical industry. Environment, Development and Sustainability, 1–28.
Tavakoli, M., Ghanavati-Nejad, M., Tajally, A., & Sheikhalishahi, M. (2023). LRFM—based association rule mining for dentistry services patterns identification (case study: a dental center in Iran). Soft Computing, 1–16.
Tavakoli, M., Tavakkoli-Moghaddam, R., Mesbahi, R., Ghanavati-Nejad, M., & Tajally, A. (2022). Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study. Medical & Biological Engineering & Computing, 60(4), 969–990.
Tavana, M., Kian, H., Nasr, A. K., Govindan, K., & Mina, H. (2022). A comprehensive framework for sustainable closed-loop supply chain network design. Journal of Cleaner Production, 332, 129777.
Urı́a, M. V. R., Caballero, R., Ruiz, F., & Romero, C. (2002). Meta-goal programming. European Journal of Operational Research, 136(2), 422–429.
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.
