Quantum Computing And Machine Learning For Efficiency Of Maritime Container Port Operations; Implementation Of Quantum Computing And The Effect On The Surrounding Community
Roberts, James, School of Engineering and Applied Science, University of Virginia
Lambert, James, EN-Eng Sys and Environment, University of Virginia
Baritaud, Catherine, EN-Engineering and Society, University of Virginia
The port systems across the world are experiencing delays unlike any time period before as a result of COVID-19. The Port of Virginia has recognized the importance of resiliency to delays as well as the environmental footprint of the operation, and the capstone team from The University of Virginia analysis has been requested to help the port better achieve their goals. The focus of the team is to use emerging methods of computing to optimize The Port of Virginia while also developing solutions that will help to meet the port’s climate goals. The key climate goal set by The Port of Virginia is to reduce net carbon emissions to zero by 2040; therefore, the capstone team will develop solutions to help the port reach the goal by 2040 and develop more resilient solutions to optimize the port. The implementation of emerging computing methods will provide many changes to daily operations of the port as well as the surrounding community. The STS analysis will focus on locating the areas in which the surrounding community will see alterations in daily life. The technical research into emerging computing methods application at the port and the STS analysis of the effect on the surrounding communities are tightly coupled. This relationship occurs because the port is one of the largest employers in the region and many localities rely on port operations for business.
The research process into emerging computing methods began with a focus on quantum computing. Quantum computing allows for far faster computing times than classical computers due to the lack of binary gates but rather qubits. This capability was intriguing to the capstone team because many of the port’s complex operations cannot be solved using classical computers. The technology of quantum computing is still relatively new; therefore, other emerging computing methods that can be run in parallel with quantum computing, such as neural networks, were researched. The use of neural networks allows for operations at the port such as container stacking to be optimized under specific disruption scenarios where typical operations are insufficient.
The results of the research performed by the capstone team were predominantly focused on the implementation of neural networks to model the container stack. The team developed and trained a model to select and move five containers within a stack. Upon completion of the model the team found the neural network was able to train itself and begin the optimization for the reduction of required moves. The constraint in the model results was computing capability, and this solidified the need for increased research into quantum computing as a potential emerging computing technology to benefit the port.
The proposed changes occurring at The Port of Virginia provide an opportunity for every day life of those who live near the port to change. The STS analysis was conducted by researching areas that will improve or negatively alter the environment, traffic, and workload of employees. An actor network theory analysis as developed by Law and Callon was used to determine the relationship between all groups involved with the port.
The STS research found that the implementation of new computing methods will lead to shorter wait times for trucks and ships while at the port. This will provide a more streamlined flow of vehicles in and out of the port as well as reduce harmful emissions in the environment. The use of barges and regenerative cranes will also decrease emissions and the dependency on the shared energy grid. The analysis allowed the team to research methods to better achieve the goal of optimization while also reducing the environmental footprint of the port.
The capstone team’s research into emerging computing methods that can optimize the port is beneficial to increasing container throughput; however, the research is not limited to the port, the community must also be considered when large changes to port operation occur.
BS (Bachelor of Science)
Quantum Computing, Neural Network, Actor Network Theory, Vehicle Emissions, Container Stack Optimization
School of Engineering and Applied Science
Bachelor of Science in Systems Engineering
Technical Advisor: James Lambert
STS Advisor: Catherine Baritaud
Technical Team Members: Ibrahim Hamdy, Maxwell St. John, Sidney Jennings, Tiago Magalhaes
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