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    AI-Based Optimization Framework for Scheduling Autonomous Rail-Guided Vehicles in Warehouse Systems
    (2026-01-01)
    Keawchai, Rattanaphapra
    ;
    Yanyong, Sarucha
    Scheduling 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.
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    A modified marriage in honey-bee optimization for multiobjective optimization problems
    (2012-11-28)
    Poolsamran, Patcharawadee
    ;
    Thammano, Arit
    This paper proposes a modified marriage in honey-bee optimization for solving multiobjective optimization problems. Unlike the original marriage in honey-bee optimization, the proposed algorithm divides the objective space into several colonies, each of which has its own queen. The fitness of each solution is based on 3 parameters: the size of the colony, the number of dominating solutions, and the number of dominated solutions. The nondominated solutions with highest fitness values are preferentially assigned to be the queens while the rest are assigned to be the drones. Next, all drones are assigned to the colony according to their distances from the queens of the colonies. In order to maximize a genetic variance in the population, the multiple mating is used. The multiple mating requires the queen to mate with drones from the other colonies. The proposed algorithm has been evaluated and compared to two state-of-the-art metaheuristic algorithms: the Pareto archived evolution strategy and the nondominated sorting genetic algorithm. The experimental results on 5 different ZDT benchmark functions illustrate that the proposed algorithm is able to converge to the true Pareto fronts and has better spread of solutions, as compared with the published results of the two state-of-the-art algorithms. © 2012 IEEE.