Cost-Aware Heterogeneous Service Placement Strategies for MEC-Based Unmanned Delivery

Jia Xu, Mengmeng Zhu, Xiao Liu, Xuejun Li, Yun Yang

Abstract: Unmanned delivery utilizes autonomous vehicles, such as drones or unmanned ground vehicles (UGVs), to transport goods, parcels, or other materials without human intervention which can significantly reduce delivery costs and time. Servitised drones and UGVs provide flexible, on-demand delivery services that optimize resource usage, improve logistics efficiency. Drone-as-A-Service (DaaS), as a typical paradigm of unmanned delivery, can maximize delivery efficiency while minimizing costs. Most existing DaaS systems only support modeling for a single type of delivery service, overlooking the diversity of available delivery services. In fact, the collaboration between different types of heterogeneous delivery services can not only reduce delivery costs but also enhance user satisfaction. However, heterogeneous services require necessitating effective service placement strategies to optimize the allocation and coordination of service resources, ultimately improving delivery efficiency and cost-effectiveness. Therefore, how to generate a suitable service placement plan with the objective of minimizing costs, has become a significant challenge. In this paper, a multi-access edge computing based heterogeneous delivery service framework (HDaaS) is proposed for effectively managing heterogeneous delivery resources. Building upon this framework, we design a cost-aware delivery service placement strategy (CDS) consisting of two phases: recommendation phase using CDSR-GA to identify optimal service and placement phase employing CDSP-RL to generate and refine placement plans. Experimental results show that the CDS strategy can generate delivery service placement plans that effectively reduce service provider costs under deadline constraints by about 29.84% on average.

External IDs:doi:10.1109/tnsm.2025.3598812