AI-Based Optimization Framework for Scheduling Autonomous Rail-Guided Vehicles in Warehouse Systems

Simple item page
dc.contributor.authorKeawchai, Rattanaphapra
dc.contributor.authorYanyong, Sarucha
dc.date.accessioned2026-08-06T10:53:51Z
dc.date.available2026-08-06T10:53:51Z
dc.date.issued2026-01-01
dc.description.abstractScheduling tasks for autonomous Rail-Guided Vehicle (RGV) systems presents a complex optimization challenge that critically influences warehouse automation performance. This research develops an AI-based RGV scheduling framework that allows configuration of robot parameters such as maximum velocity, acceleration, deceleration, and track dimensions, accounting for velocity constraints imposed by curved tracks. The study includes five computational intelligence algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), hybrid GAPSO, and hybrid SUPER-SAPSO. The framework integrates path planning, layered collision penalty models, and multi-RGV task assignment under a physics-based travel time model while minimizing RGV idle time and addressing workload imbalance. Experimental results demonstrate comparative analyses of the efficiency and convergence speed of the various computational intelligence algorithms in optimizing overall warehouse efficiency.
dc.identifier.citationKst 2026 18th International Conference on Knowledge and Smart Technology, 150-155, 2026
dc.identifier.doi10.1109/KST67832.2026.11432405
dc.identifier.other2-s2.0-105036866349
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17671
dc.sourceKst 2026 18th International Conference on Knowledge and Smart Technology
dc.subjectautomated logistics
dc.subjectautonomous mobile robot
dc.subjecthybrid optimization
dc.subjectRGV
dc.subjectswarm intelligence
dc.subjectwarehouse automation
dc.titleAI-Based Optimization Framework for Scheduling Autonomous Rail-Guided Vehicles in Warehouse Systems
dc.typeConference Paper

Files

Collections

Research