Journal of Humanitarian Logistics and Supply Chain Management · 2024 · 31 citations · 39 references
ManagementOperations ManagementSupply Chain ManagementDisaster PreparednessDisaster ResponseEmergency ManagementDisaster Preparedness DecisionsDisaster RecoveryDisaster Response PlanningHumanitarian ReliefSupply Chain RecoveryDisaster Supply Chain ManagementEpsilon-constraint SchedulingHumanitarian Relief OperationsModel Performance EvaluationPost-disaster Decision-makingPost-disaster Relief OperationsResilient Sustainable Supply ChainsScenarios
Purpose Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief. Design/methodology/approach A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations. Findings A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained. Originality/value Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.
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Closed-loop optimization of fast-charging protocols for batteries with machine learning
Peter M. Attia, Aditya Grover, Norman Jin et al. · Nature · 2020 · 1.1K citations · Full text
Engineering, Computer Science, Electrochemical Engineering +15