Over the years, the global supply chain has evolved into a more extensive interconnected complex network with multiple suppliers, manufacturers, and customers. Since environmental issues have become a burning question in recent years, the focus has shifted to attaining sustainability in supply chain management. The green supply chain or sustainable network is a concept to reduce environmental impacts in the life cycle of a product. However, green supply chain management is often challenged with additional operating costs and difficulty monitoring the implications within the complex network system. Additionally, many stakeholders are unaware of the importance of sustainability analysis, which eventually complicates adopting green cultures in actual applications. Since green supply chain management deals with multiple aspects, such as cost and carbon emission, the multiobjective optimization method is widely used to evaluate supply chain performance. This paper intensively reviews the state-of-the-art literature on applying multiobjective optimization techniques in green supply chain management. The study highlights aspects of green supply chain structures, model formulation techniques considering multiple objectives simultaneously, and solution methods for multiobjective optimization problems. Finally, a conclusion is drawn with the scope of the potential research opportunities for integrating economic and environmental considerations in sustainable supply chain management practice.
Maintaining smooth traffic during disaster evacuation is a lifesaving step. Traffic resilience is often used to define the ability of a roadway during disaster evacuation to withstand and recover its functionality from disturbances in terms of traffic flow caused by a disaster. However, a high level of variances due to system complexity and inherent uncertainty associated with disaster and evacuation risks poses great challenges in predicting traffic resilience during evacuation. To fill this gap, this study aimed to propose a new integrated data-driven predictive resilience framework that enables incorporating traffic uncertainty factors in determining road traffic conditions and predicting traffic performance using machine learning approaches and various space and time (spatiotemporal) data sources. This study employed an augmented Long Short-Term Memory (LSTM)-based approach with correlated spatiotemporal traffic data to predict traffic conditions, then to map those conditions to traffic resilience levels: daily traffic, segment traffic, and overall route traffic. A case study of Hurricane Irma’s evacuation traffic was used to demonstrate the effectiveness of the proposed framework. The results indicated that the proposed method could effectively predict traffic conditions and thus help to determine traffic resilience. The data also confirmed that the traffic infrastructures along the US I-75 route remained resilient despite the disturbances during the disaster evacuation activities. The findings of this study suggest that the proposed framework is applicable to other disaster management scenarios to obtain more robust decisions for the emergency response during disaster evacuation.
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