University of Tehran Press Industrial Management Journal 3115-7386 16 4 2024 11 21 Designing a Resilient Three-level Intertwined Supply Network under Disruption and Uncertainty طراحی شبکۀ تأمین درهم‌آمیخته سه سطحی تاب‌آورتحت اختلال و عدم قطعیت 502 534 99722 10.22059/imj.2024.381366.1008177 EN Farahnaz Rahmani Meybodi Ph.D. Candidate, Department of Industrial Management, Faculty of Industrial Management and Information Technology, Shahid Beheshti University, Tehran, Iran. 0009-0000-0088-6479 Akbar Alem Tabriz Prof., Department of Industrial Management, Faculty of Industrial Management and Information Technology, Shahid Beheshti University, Tehran, Iran. 0000-0002-4562-8032 Mostafa Zandiyeh Prof., Department of Industrial Management, Faculty of Industrial Management and Information Technology, Shahid Beheshti University, Tehran, Iran. 0000-0003-1209-9514 Davood Talebi Assistant Prof., Department of Industrial Management, Faculty of Industrial Management and Information Technology, Shahid Beheshti University, Tehran, Iran. 0000-0002-2780-8320 Journal Article 2024 08 24 <strong>Objective</strong> The intertwined supply chain refers to a network of interconnected supply chains that, through long-term collaboration, can deliver products and services to multiple customers, even in the face of various disruptions. In the case of the tiles and ceramics industry, it is essential to enhance resilience and integration across all chains to address issues such as capacity limitations at centers, which are often due to the unavailability of raw materials and production machinery caused by sanctions. These challenges have led to inadequate production management and increased costs. The aim of this research is to design a resilient three-level intertwined supply network capable of withstanding disruptions and uncertainty while simultaneously increasing profit and enhancing resilience.   <strong>Methods</strong> This research employs a capacity development strategy and supplier separation to address disruptions at centers and uses fuzzy chance-constrained programming to tackle demand uncertainty. The model of this research is a multi-product, multi-period, mixed-integer linear programming model. To solve the bi-objective optimization (profit and risk), the augmented epsilon constraint method was used, resulting in a Pareto solution set. To validate the model, real data related to the intertwined supply network of tiles and ceramics (interconnected supply chains of tiles and ceramics, glaze, and alumina balls) were used, and the model was solved using GAMS software.   <strong>Results</strong> Based on the model output and the results of the decision variables, profit increased by three percent in the deterministic case compared to the uncertain case, while risk decreased by seven percent. The numerical results of the sensitivity analysis show that changing the epsilon parameter from zero to five-hundredths and from five-hundredths to one-tenth leads to a two to seven percent increase in profit. Additionally, the optimal capacity increase in the alumina ball production center is twenty percent, in the tile production center is ten percent, and in the glaze production center is thirty percent.   <strong>Conclusion</strong> In the current global context, where the evolution of supply chains is moving towards intertwined supply networks, there are very few studies conducted on these networks. Given the nature of the intertwined supply network, the presented general model facilitates the transfer of raw materials or products through both intra-chain relationships (such as between suppliers and producers, and between producers and customers) and inter-chain relationships (such as between suppliers of one chain and those of another, between producers of one chain and those of another, between customers (production centers) of one chain and customers (final) of another, and the reverse relationship from producers of one chain to suppliers of another). The model simultaneously considers strategic decisions, including the selection of centers that require capacity increases, while optimizing operational decisions (such as production levels, inventory levels, and transportation levels) accordingly. https://imj.ut.ac.ir/article_99722_a42bbffa22d2749c7ece242efbccc4d5.pdf