Today, supply chains are exposed to a variety of risks. Ignoring these risks can cause irreparable damage to them. On the other hand, the subject of redesigning is essential when the supply chain loses its optimality or needs to be altered due to changing conditions. In this paper, in contrast to most researches done in the literature, the problem of resilient supply chain network redesign is investigated under operational and disruption risks. The network structure addressed in this paper is a mixture of open and closed loop schemes, which has been rarely considered in the literature. A novel stochastic robust optimization model is developed to manage the uncertainty of the problem. The problem is formulated as a linear mixed-integer programming model with the objective function of profit maximization. Due to the high complexity of the model and the challenge to solve it in large-scale dimensions, a Lagrangian relaxation algorithm is developed, and its excellent performance is shown by the relevant calculations. In order to measure the efficiency and validity of the model, a case study has been presented in the automotive tire industry. The results show that using resilience strategies is very effective in improving the profitability of the supply chain and preventing losses. In addition, the use of a mixed supply chain network increases the overall profitability of the supply chain in comparison to a forward supply chain network.
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
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