Designing a Resilient Closed-Loop Supply Chain Network under Operational Risk and Disruption Conditions by the Mulvey Approach

Document Type : Research Paper

Authors

1 Ph.D. Candidate., Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

2 Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

3 Associate Prof., Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

4 Assistant Prof., Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

Abstract

Objective: While the closed-loop supply chain network was created launch, to design, and exploit the flow of materials between chain centers, supply chains face different risks, including operational ones and disruption. Each of such risks can lead to irreparable damage. Therefore, designing or redesigning supply chains to make them resilient against different risks is one of the most important programs that potentially affect the supply chain’s performance. The purpose of this research  is to design a resilient closed-loop supply chain network under the operational risks and disruption conditions by the Mulvey approach in Tehran's food industry companies with multi-products.
Methods: In this research, the problem of the resilient closed-loop supply chain is considered as a network of nodes (production sites) that are connected by arcs (paths). The model is formulated as an integer programming, the objective function of which involves maximizing the number of nodes in estimating demand and minimizing costs based on a series of scenarios developed by the Mulvey approach.
Results: In the practical phase, first, a closed-loop supply chain consisting of 10 manufacturers, 300 distributors, and two types of products was considered for modeling. Then, three more important scenarios with probabilities of 0.3, 0.2, and 0.5 were developed to present different amounts of customer demand and facilities capacity depending on the number of disruptions. Solving the problem for the multi-product food companies, using the LP metric model showed that despite no disruption in the supply chain, the robust optimal solutions for the first and second objective functions were equal to 99.484 and 790.50, respectively. In terms of manufactured products; in the first node, the amounts of products 1 and 2 did not change for the first and second scenarios but changed by 1.4 and 2.5 units in the third scenario. In the third node, the amount of product 1 did not change for the first scenario, but for the second and third scenarios, it changed by 10.60 and 6.8, respectively. The amount of product 2 did not change for the first and third scenarios but changed by 7.7 for the second scenario. In the 10th node, the amounts of products 1 and 2 did not change for the second scenario, but the amount of product 1 changed by 2.8 and 2.3 for the first and third scenarios. In addition, the amount of product 2 changed by 10.3 and 2.8 for the first and third scenarios, respectively. In the other nodes, the amounts of the products did not change. For both of the products in different nodes, except in nodes 4, 6, 7, and 9, and product 1 in node 8, some problems had to be fixed.
Conclusion: According to the findings, developing the proposed model reduces operational risks and disruption as the most important causes of inefficiency in the supply chain. So, it is necessary to design robust and resilient supply chains in all industries, especially in the food industry due to its significance.

Keywords

References

Accorsi, R. & Baruffaldi, G. & Manzini, R. (2020). A closed-loop packaging network design model to foster infinitely reusable and recyclable containers in food industry. Sustainable Production Consump, 24: 48 – 61.
Aghaie, A. & Hajian-Heidary, M. (2019). Simulation-based optimization of a stochastic supply chain considering supplier disruption: Agent-based modeling and reinforcement learning. International Journal of Science & Technology, Scientia Iranica, 26 (6), 3780 – 3795.
 (in Persian)
Aghezzaf, E. H. & Sitompul, C. & Najid, N.M. (2010). Models for robust tactical planning in multi-stage production systems with uncertain demands. Computers & Operations Research, 37 (5): 880 - 889.
Alem Tabriz, A. & Pishvaee, M.S. & Mohammadi, A.S. (2018). Designing Green Closed-loop Supply Chain Network with Financial Decisions under Uncertainty. Industrial Management Journal, 10 (1), 61 - 84. (in Persian)
Amin-Tahmasbi, H. &, Raheb, M. & Jafariyeh, S. (2018). A Green Optimization Model in the Closed-Loop Supply Chain with the Aim of Increasing Profit and Reducing Environmental Problems, with Regard to Product Guaranty Period. Journal of Operations Research and Its Applications, 58 (3), 27 - 44. (in Persian)
Baghersad, M. & Zobel, C.W. (2021). Assessing the extended impacts of supply chain disruptions on firms: An empirical study. International Journal of Production Economics, 231, Art. No. 107862.
de Oliveira, U.R. & Marins, F.A.S. & Rocha, H. M. & Salomon, V.A.P. (2017). The ISO 31000 standard in supply chain risk management. J. Cleaner Prod, 151: 616 – 633.
Dixit, V. & Verma, P. & Tiwari, M.K. (2020). Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure. International Journal of Production Economics, 227, Doi: 10.1016/j.ijpe. 107655.
Dolgui, A. & Ivanov, D. (2020). Ripple effect and supply chain disruption management: new trends and research directions. Int. J. Prod. Res, 59 (1): 102 – 109.
Fathollahi-Fard, A.M. & Hajiaghaei-Keshteli, M. & Tian, G, & Li, Z. (2020). An adaptive Lagrangian relaxation-based algorithm for a coordinated water supply and wastewater collection network design problem. Information Science, 512: 1335 – 1359.
Ghorbanpour, A. & Jamali, Gh.R. & Mousavi, M. (2021). A Green-resilient Supply Chain Network Optimization Model in Cement Industries. Industrial Management Journal, 13 (2), 222 - 245. (in Persian)
Hassanzadeh, A.S. & Zhang, G. A. (2013). Multiobjective Facility Location Model for Closed-loop Supply Chain Network under Uncertain Demand and Return. Applied Mathematical Modelling, 37 (6): 4165 – 4176.
Ivanov, D. & Sethi, S. & Dolgui, A. & Sokolov, B. (2018). A survey on control theory applications to operational systems, supply chain management, and industry 4.0. Annu. Rev. Control, 46: 134 – 147.
Mondal, A. & Kumar Roy, S. (2021). Multi-objective sustainable opened- and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation. Computers & Industrial Engineering, 159: 107 453.
Mulvey, J.M. & Ruszczyński, A. (1995). A new scenario decomposition method for large-scale stochastic optimization. Operations research, 43 (3), 477 - 490.
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.
Pavlov, D. & Ivanov, A. & Dolgui, B. & Sokolov, B. (2018). Hybrid fuzzy probabilistic approach to supply chain resilience. IEEE Transactions on Engineering Management, 65 (2): 303 – 315.
Piraveenan, M. & Jing, H. & Matous, P. & Todo, Y. (2020). Topology of international supply chain networks: A case study using factset revere datasets. IEEE Access, 8: 154540 – 154559.
Ramezani, M. & Bashiri, M. & Tavakkoli-Moghaddam, R. (2012). A Robust Design for a Closedloop Supply Chain Network under an Uncertain Environment. The International Journal of Advanced Manufacturing Technology, 66 (5-8), 825 - 843.
Sabouhi, F. & Pishvaee, M.S. & Jabalameli, M.S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657 – 672.
Safar, M.M. & Shakuri-Ganjavi, H. & Razmi, J. (2014). Designing a green closed loop supply chain by considering operational risks under uncertainty and solving with NSGA II algorithm. Industrial Engineering Specialized Journal, 49 (1): 55 – 68. (in Persian)
Saglam, Y.C. & Çankaya, S.Y. & Sezen, B. (2020). Proactive risk mitigation strategies and supply chain risk management performance: An empirical analysis for manufacturing firms in Turkey. Journal of Manufacturing Technology Management, 32 (6), 1224 - 1244.
Shabbir, M.S., Mahmood, A., Setiawan, R., Nasirin, C., Rusdiyanto, R., Gazali, G., Arshad, M.A., Khan, S. & Batool, F. (2021). Closed-loop supply chain network design with sustainability and resiliency criteria. Environmental Science and Pollution Research, Doi: 10.1007/s11356-021-12980-0.
Sibevei, A. & Azar, A. & Zandieh, M. (2021). Developing a Two-stage Robust Stochastic Model for Designing a Resilient Blood Supply Chain Considering Earthquake Disturbances and Infectious Diseases. Industrial Management Journal, 13 (4), 664 - 703. (in Persian)
Talaei, M. & Farhang-Moghaddam, B. & Pishvaee, M.S. & Bozorgi-Amiri, A. & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of Cleaner Production, 113: 662 - 673.
Temoçin, B.Z. & Weber, G.W. (2014). Optimal control of stochastic hybrid system with jumps: a numerical approximation. J. Comput. Appl. Math, 259: 443 - 451.
Tian, Y. & Shi, Y. & Shi, X. & Li, M. & Zhang, M. (2021). Research on Supply Chain Network Resilience Considering the Exit and Reselection of Enterprises. IEEE ACCESS, Digital Object Identifier, DOI:10.1109/ACCESS.2021.3090332.
Tirkolaee, E.B. & Goli, A. & Weber, G.W. (2019). Multi-objective Aggregate Production Planning Model Considering Overtime and Outsourcing Options under Fuzzy Seasonal Demand. Advances in Manufacturing II. Springer, Cham, 81 -96.
Tirkolaee, E.B. & Mahdavi, I. & Esfahani, M.M.S. & Weber, G.W. (2020). A robust green-allocation-inventory problem to design an urban waste management system under uncertainty. Waste Manag, 102: 340 - 350.
Toorajipour, R. & Sohrabpour, V. & Nazarpour, A. & d Oghazi, P. & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122: 502 – 517.
Vali-Siar, M.M. & Roghanian, E. & Jabbarzadeh, A. (2022). Resilient mixed open and closed-loop supply chain network design under operational and disruption risks considering competition: A case study. Computers & Industrial Engineering, 172: Part A, October 2022, 108513.
Wacker, J.G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16: 361 – 385.
Zahedi, A. & Salehi-Amiri, A. & Hajiaghaei-Keshteli, M. & Diabat, A. (2021). Designing a closed-loop supply chain network considering multi-task sales agencies and multi-mode transportation. Soft Computing, Doi: 10.1007/s00500-021-05607-6.
Zamanian, A. & Nasrollahi, M. & Fathi, M. R. (2019). Mathematical Modeling of Sustainable Supply Chain Networks under Uncertainty and Solving It Using Metaheuristic Algorithms. Industrial Management Journal, 11 (4), 621 – 652. (in Persian)
Zare-Mehrjerdi, Y. & Shafiee, M. (2020). Multiple-Sourcing in Sustainable Closed-loop Supply Chain Network Design: Tire Industry Case Study. International Journal of Supply and Operations Management, 7 (3), 202 – 221. (in Persian)
Zare-Mehrjerdi, Y. & Shafiee, M. (2021). A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. Journal of Cleaner Production, 289, 125 - 141.
Zereshki, N. & Momeni, M. (2020). 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)
Zhang, C. & Tian, G. & Fathollahi-Fard, A.M. & Li, Z. (2020). Interval-valued intuitionistic uncertain linguistic cloud Petri net and its application in risk assessment for subway fire accident. IEEE Trans Autom Sci Eng, Doi: 10.1109/TASE.3014907.
Industrial Management Journal
Volume 14, Issue 4
2023
Pages 595-617

Files

Share

How to cite

Statistics