With the rapid development of digital technology, the smart sensor-based container equipment and intelligent logistics operations contribute to achieving the efficiency improvement and sustainability achievement of container supply chain under the IoT-based logistics 4.0 scenarios. This paper tries to study the state-of-the-art knowledge of the container logistics supply chain management motivated by digital techniques. Through data-driven analysis this review is performed to assist researchers and practitioners to better understand the container logistics management. The integrated research framework is designed by developing a bibliometric analysis study to address the research themes of the container logistics era. The related publications from the Web of Science database from 2003 to 2022 were indexed and 2897 reference samples are collected as the research data. In addition, the VosViewer is adopted to portray the network, co-occurrence, and co-word analysis by visualizing the collaborative relationships of collected samples. The results show that digital technology has been widely applied in container logistics supply chain management practices, contributing to resilience and sustainability improvement by intelligent operations. These research findings are also helpful for researchers by providing a deep penetrating insight into research opportunities and great potentials of container logistics supply chain by innovative digital technology-enabled practices.

In this paper, we present a stochastic drone-scheduling problem where the energy consumption of drones between any two nodes is uncertain. Considering uncertain energy consumption as opposed to deterministic energy consumption can effectively enhance the safety of drone flights. To address this issue, we developed a two-stage stochastic programming model with recourse cost, and we employed a fixed-sample sampling strategy based on Monte Carlo simulation to characterize uncertain variables, followed by the design of an efficient variable neighborhood search algorithm to solve the model. Case study results indicate the superiority of our algorithm over genetic algorithms. Additionally, a comparison between deterministic and stochastic models suggests that considering the uncertainty in energy consumption can significantly enhance the average returns of unmanned aerial vehicle scheduling systems.

The distribution of scientific project funds is usually based on manual allocation, which is inefficient. Other automatic allocation methods are difficult to balance projects in different fields. In this study, we first utilize the adaptive similarity fitting method, leveraging historical project data to construct an input–output fitting model. Subsequently, we align the input–output model with data from projects awaiting funding through the application of scaling factors. Viewing project funds distribution as a multi-objective optimization problem, we employ the NSGA-II algorithm for optimization. Cases in a certain region illustrate the efficacy of our approach in the efficient distribution of research project funds, addressing the diverse preferences of decision-makers. After applying our method to reassign funds for a research grant project in a certain region, while keeping the total funding amount unchanged, the research and talent output in the region for the year 2020 are expected to increase by 10.63% and 6%, respectively. Similarly, for the year 2021, the increases in research and talent output are 6.09% and 6.64%. The total funding amount for the year 2020 can be reduced by 11.67% with the output stays the same, and for 2021, the funding amount can be reduced by 7%.