Preserving Privacy in Logistics by Using Swarm Intelligence from the Bottom-Up
Marija Gojković, Melanie Schranz · 2024
Current logistics companies carry out internal optimizations concerning resource utilization of their freighters. In cooperation with other companies, e.g., to hand over the orders, they use a trustworthy broker that handles this process without exchanging company-related, private information from the logistics companies to not violate confidentiality. Increasing digitalization offers the opportunity to carry out cross-company optimizations and resource allocation. Such optimizations would increase the average load factor of transport providers, and ultimately reduce the overall carbon footprint of the entire transport sector. However, the companies are typically competitors and sharing trade secrets could result in competitors overtaking the market, leading logistics companies to not collaborate. This paper proposes a novel application of swarm intelligence as privacy enabler in the logistics sector. Logistics companies and their resources are modeled as agents in a simulated swarm system where agents interact by following simple local rules. Such agent behavior emerges into a meaningful solution that provides a secure and efficient transportation network. Applying swarm intelligence with local rules from the bottom-up, the paper proposes a secure multi-party optimization in the logistics sector, ensuring that no participant needs to reveal any sensitive business information to any other participant in the system, including any third parties or brokers, thereby immediately overcoming the companies' reluctance to participate. The agent's behavior is inspired by the Artificial Bee Colony Algorithm (ABC). We show that reducing the amount of exchanged data with swarm algorithms still leads to a satisfying optimization in terms of resource utilization of all involved parties.