Maintaining effective logistics management during and after COVID‑19 pandemic: survey on the importance of artificial intelligence to enhance recovery strategies

OPSEARCH(2024)

引用 0|浏览0
暂无评分
摘要
The outbreak of coronavirus (COVID-19) has forced governments around the world to limit the movement of people and prohibit cross-countries travel or activities. However, the policymakers have been mobilizing a lot of effort to organize the transactions of essential goods and services to tackle the pandemic outcomes. These worldwide efforts indicate the importance of establishing and maintaining accurate management of logistics operations either during the current crisis or the consecutive periods. Motivated by the vital importance of logistics operations during the speedy increase of COVID-19 across the globe, this study critically investigates the existing works closely related to it. The main objective is to identify and boost the understanding of the characteristics of the logistics that play a critical role during pandemics. The collection of the literature was performed employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology. Moreover, a multilateral framework was used to classify the selected works. The findings of this study indicate that academic works have focused mostly on “post-event” (47.24%) management of logistics operations followed by the “pre-event” (32.76%) and least in the “Integrated” (20%.) methods. Moreover, the analysis of these results provided valuable insights that are discussed in detail. Furthermore, numerous key areas have been recognized that need proper attention to adjust the overall efficiency of the current logistics operations using intelligent solutions. We believe that the findings of this study would be useful to enhance the decision-makers. furthermore, maintaining efficient sustainable logistics systems during and after a crisis remains essential.
更多
查看译文
关键词
Coronavirus/COVID-19,Smart logistics,Artificial intelligence,Emergency control,Decision-making,Management
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要