Designing a resilient supply chain network with a decentralized strategy under uncertainty
Abstract
This paper investigates the vulnerability of supply chains to unforeseen disruptions and proposes a novel decentralized approach to enhance resilience. Traditional centralized supply chains are prone to risks, and this study introduces a decentralized model where individual entities make decisions based on local information, improving overall performance. The primary aim is to optimize a three-level resilient supply chain using a multi-objective mathematical model to minimize operational costs and promote decentralization. To address this, the study utilizes three optimization algorithms namely NSGA-III, bat, and whale algorithms where NSGA-III proved most effective by providing the highest number of Pareto-optimal solutions. The bat algorithm showed weaker performance across various metrics. A detailed sensitivity analysis was also conducted, revealing that increasing cost parameters, such as construction, ordering, and transportation, enhances decentralization. For example, a 50% rise in construction costs led to a 40% improvement in decentralization. This research highlights the potential of decentralized models in optimizing supply chain resilience.
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