Abstract for: Using AI for Container Port Infrastructure Development: Alternative Multisolving Strategies

Container port ecosystems and allied infrastructure play a central role in global trade, with the efficiency of the involved supply networks and logistics, along with potential bottlenecks, being central themes in disciplines such as operations management. We conduct a literature review to show that, notwithstanding a plethora of approaches towards calculating the optimal operations performance, the involvement of multiple stakeholders (e.g., inland carriers, customs, and advocates for sustainable transportation) with conflicting interests have not been investigated from an integrated perspective. Motivated by the container handling operations at the port of Thessaloniki, Greece, this research applies Systems Thinking to investigate the complex interconnections and feedback loops associated with Artificial Intelligence (AI) driven infrastructure on operations efficiency and environmental sustainability of container ports under a “multisolving” perspective. We find that AI can catalyze infrastructure development and balance the associated multiple trade-offs in container port systems at the short- and long-term horizon. We also find that multisolving in such a system can be implemented across two alternative strategies: (i) adjusting the input resources to control key stocks; or (ii) altering the weight on decisions that are critical in influencing the outcome trade-offs.