Designing a Multi-Product Supply Chain Model Despite Disruption in the Supplier With A Stable Mathematical Optimization Approach in Refinery Maintenance Industries
Subject Areas : Industrial Management
Amir Rahimimanesh
1
(PhD Candidate in Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran)
Hamzeh Amin-Tahmasbi
2
(Associate Professor, Department of Industrial Engineering, East Faculty of Technology and Engineering, Gilan University, Rudsar, Iran)
Kambiz Shahroudi
3
(Associate Professor, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran)
Keywords: Supply Chain, Robust Mathematical Optimization, Disturbance, Random Two-Stage Model.,
Abstract :
The supply chain network design problem includes strategic decisions that significantly impact tactical and operational configurations and decisions. The purpose of this research is to provide a multi-product supply chain model that addresses supplier disruptions through a mathematical optimization approach. In line with multi-product supply chain management, there is a need to supply items and raw materials for use in processes, and the supply of these items is subject to uncertainty. Specifically, suppliers may not provide part of the ordered demand to the customer at the required time. To address this uncertainty, two types of suppliers are considered. The first category consists of cheap but unreliable suppliers, while the second category includes reliable suppliers who are more expensive than the first. Items received from suppliers are used in the production or repair process, and a documented model should be provided to manage this process. To integrate these decisions into a cohesive model, previous articles have utilized a random two-stage decision-making model, employing the sampling average approximation method to solve the proposed problem. In this research, due to the dependence of the random two-stage model on non-deterministic parameters (or the worst-case scenario), a robust mathematical model has been developed for the two-stage random model. Finally, the stable model provides the decision-maker with the opportunity to choose the parameters according to the degree of importance of each component.
Amin-Tahmasbi, H., Raheb, M., & Jafariyeh, S. (2018). A green optimization model in closed-loop supply chain with the aim of increasing profit and reducing environmental problems, with regard to product guaranty period. Journal of operational research in its applications (applied mathematics)-Lahijan Azad University, 15(3), 27-44.
Beheshtinia, M. A., & Nemati-Abozar, V. (2017). A Combination of Fuzzy AHP and TOPSIS Method for the Supplier Selection Problem (Case Study: Advertising Company). Journal of Modeling in Engineering, 15(48), 217-229.
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.
Dickson, G. W. (1966). An analysis of vendor selection systems and decisions. Journal of purchasing, 2(1), 5-17.
Fallah, P., Rabiee, M., Yousefi-Babadi, A., Roghanian, E., & Hajiaghaei-Keshteli, M. (2023). Designing an Agile, flexible and resilient disaster supply chain network using a hybrid group decision-making robust optimization framework. Computers & Industrial Engineering, 184, 109591.
Firouzi, F., Baglieri, E., & Jaber, M. Y. (2014). Two-product inventory management with fixed costs and supply uncertainty. Applied Mathematical Modelling, 38(23), 5635-5650.
Gaballa, A. A. (1974). Minimum cost allocation of tenders. Journal of the Operational Research Society, 25(3), 389-398.
He, J., Alavifard, F., Ivanov, D., & Jahani, H. (2019). A real-option approach to mitigate disruption risk in the supply chain. Omega, 88, 133-149.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International journal of production research, 58(10), 2904-2915.
Jabbarzadeh, A., Fahimnia, B., Sheu, J. B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149.
Jabbarzadeh, A., Jalali Naini, S. G., Davoudpour, H., & Azad, N. (2012). Designing a supply chain network under the risk of disruptions. Mathematical Problems in Engineering, 2012(1), 234324.
Kim, J., Qiu, R., Jon, J., & Sun, M. (2024). Multi-objective programming for multi-period multi-product closed-loop supply chain network design: a fuzzy robust optimization approach. Environment, Development and Sustainability, 1-37.
Leung, S. C., & Wu, Y. (2004). A robust optimization model for stochastic aggregate production planning. Production planning & control, 15(5), 502-514.
Leung, S. C., Tsang, S. O., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multi-site production planning problem in an uncertain environment. European journal of operational research, 181(1), 224-238.
Malcolm, S. A., & Zenios, S. A. (1994). Robust optimization for power systems capacity expansion under uncertainty. Journal of the operational research society, 45(9), 1040-1049.
Markowitz, H. M. (1976). Markowitz revisited. Financial Analysts Journal, 32(5), 47-52.
Morales, F., Franco, C., & Mendez-Giraldo, G. (2018). Dynamic inventory routing problem: Policies considering network disruptions. International Journal of Industrial Engineering Computations, 9(4), 523-534.
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.
Prakash, S., Kumar, S., Soni, G., Jain, V., & Rathore, A. P. S. (2020). Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach. Annals of operations research, 290, 837-864.
Rahimimanesh, A., Amin-Tahmasbi, H., & shahroodi, K. (2020). Providing a mathematical optimization model for the multi-product supply chain despite the possibility of supplier disruption under sanctions (Case Study of Refinery Repair Industries). Journal of Modeling in Engineering, 18(60), 107-125.
Sadeghi, Z., Boyer, O., Sharifzadeh, S., & Saeidi, N. (2021). A robust mathematical model for sustainable and resilient supply chain network design: preparing a supply chain to deal with disruptions. Complexity, 2021(1), 9975071.
Safari, L., Sadjadi, S. J., & Sobhani, F. M. (2023). Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model. Environment, Development and Sustainability, 1-43.
Sargut, F. Z., & Qi, L. (2012). Analysis of a two-party supply chain with random disruptions. Operations Research Letters, 40(2), 114-122.
Sawik, T. (2011). Selection of supply portfolio under disruption risks. Omega, 39(2), 194-208.
Schmitt, A. J., & Singh, M. (2009, December). Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation. In Proceedings of the 2009 winter simulation conference (WSC) (pp. 1237-1248). IEEE.
Schmitt, A. J., & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International journal of production economics, 139(1), 22-32.
Shao, X. F. (2018). Production disruption, compensation, and transshipment policies. Omega, 74, 37-49.
Snyder, L. V., & Daskin, M. S. (2006). Stochastic p-robust location problems. Iie Transactions, 38(11), 971-985.
Song, D. P., Dong, J. X., & Xu, J. (2014). Integrated inventory management and supplier base reduction in a supply chain with multiple uncertainties. European Journal of Operational Research, 232(3), 522-536.
Tang, C. S. (2006). Perspectives in supply chain risk management. International journal of production economics, 103(2), 451-488.
Tomlin, B. (2009). Impact of supply learning when suppliers are unreliable. Manufacturing & Service Operations Management, 11(2), 192-209.
Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation research part e: logistics and transportation review, 79, 22-48.
Verweij, B., Ahmed, S., Kleywegt, A. J., Nemhauser, G., & Shapiro, A. (2003). The sample average approximation method applied to stochastic routing problems: a computational study. Computational optimization and applications, 24, 289-333.
W. O. (WHO). (2002). Gender and health in disasters Geneva: World Health Organization.
Weber, C. A., Current, J. R., & Benton, W. C. (1991). Vendor selection criteria and methods. European journal of operational research, 50(1), 2-18.
Yao, M., & Minner, S. (2017). Review of multi-supplier inventory models in supply chain management: An update. Available at SSRN, 2995134.
Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International journal of production economics, 64(1-3), 385-397.
Zarrat Dakhely Parast, Z., Haleh, H., Avakh Darestani, S., & Amin-Tahmasbi, H. (2021). Green reverse supply chain network design considering location-routing-inventory decisions with simultaneous pickup and delivery. Environmental Science and Pollution Research, 1-22.
Zhen, L., Wang, K., & Liu, H. C. (2014). Disaster relief facility network design in metropolises. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(5), 751-761.
Zokaee, S., Bozorgi-Amiri, A., & Sadjadi, S. J. (2016). A robust optimization model for humanitarian relief chain design under uncertainty. Applied mathematical modelling, 40(17-18), 7996-8016.