This dissertation's primary contribution to Supply Chain and Transportation Network (SCTN) resilience lies in the implementation of a sequence of Multicriteria Mixed Integer liners programming MCMILP models that incorporate Max-Min Fairness-Inspired (MMFI) approach as a fundamental constraint, thereby delivering both effective distribution and cost-efficiency. The research's novelty is accentuated by its reliance on actual network data from Colombia's intricate multimodal transportation and supply chain system, which involves a diverse flow of multicommodity. This setting provides a robust backdrop for elucidating the real-world efficacy of the proposed models. The foundational layer of resilient supply chains and transportation networks is their capacity for an immediate and efficient response in the wake of disruptions. Chapter2 introduces the first model within this dissertation MCMILP model—that pioneers the incorporation of MMFI strategy directly into the optimization process. This innovative approach is essential for curtailing the detrimental effects of unforeseen events by guaranteeing prompt and effective resource distribution. The effectiveness of this model is not merely theoretical; its application to Colombia’s multimodal transportation data reveals a substantial enhancement in user satisfaction. By significantly reducing the performance loss across the network, the model demonstrates its exceptional capacity to uphold a high level of service and requirements, validating the concept that immediate fairness can indeed coexist with, and even bolster, operational efficiency. In Chapter 3, the spotlight shifts to the critical long-term recovery phase, where the principles of effective distribution are further operationalized to ensure sustained resilience. Here, a refined version of MCMILP model is employed, extending the concept of MMFI strategy to a more complex and temporally extended recovery process. This model proves to be a powerful tool in the unbiased distribution of resources over time, promoting a swifter restoration trajectory. The Colombian multimodal transportation system once again serves as the proving ground for this model's efficacy. The empirical results showcase not only a higher rate of network-wide satisfaction but also demonstrate that effective distribution strategies can effectively minimize the recovery timeframe. The model's application thus substantiates the assertion that MMFI integrated into recovery planning is paramount for expediting the restoration of services and maintaining a consistent satisfaction rate during periods of gradual network recovery. In Chapter 4 of the dissertation, the focus shifts toward enhancing strategic planning for enduring resilience within Supply Chain and Transportation Networks (SCTN). The chapter introduces an innovative evaluation model that incorporates MMFI, a pivotal element that significantly strengthens the model's foundation. By embedding MMFI, the evaluation framework is anchored securely in the principles of robust optimization. This methodology is particularly adept at addressing the most challenging aspects of planning and operation by emphasizing the preparation for worst-case scenarios. Such a focus ensures that the network's integrity remains uncompromised in the face of severe disruptions. The introduction of this evaluation model marks a critical phase in the dissertation, laying down a comprehensive foundation for constructing a resilient network. Unlike conventional approaches that primarily aim at survival in the aftermath of disruptions, this model elevates the network's objectives. It seeks to enable the SCTN to not just survive but to thrive amid adversities. This ambition is realized through the model's capability to maintain essential service levels, even under extreme conditions that typically would cripple unprepared networks.