Geolocation-Based Dual Service Composition Optimization Method for Manufacturing and Logistics in Cloud Manufacturing

The existing manufacturing service composition mostly focuses on the combination and optimization of manufacturing tasks, with less consideration given to the impact of different geographic locations of manufacturing services on logistics. This article proposes a manufacturing task process combination optimization model that considers geographical location. By adding logistics task nodes that connect various production tasks to the manufacturing task, the cloud manufacturing combination optimization problem can be more realistic under the drive of modern advanced logistics. Based on the model proposed in this paper, the Set-Based Particle Swarm Optimization (S-PSO) algorithm is used to address the model optimization problem. By comparing the experimental results, the effectiveness of the proposed location-based “production logistics” service composition model is proved, and its advantages in saving time and cost and improving availability reliability are verified.