Energy and Time-Efficient Scheduling of Automated Guided Vehicles System: A Hybrid Artificial Bee Colony Algorithm and Improved Ant Colony Optimization Approach.

In this paper, a hybrid implementation of the Artificial Bee Colony (ABC) algorithm and Ant Colony Optimization (ACO) is applied for optimal scheduling and path-planning for an automated guided vehicle system (AGVS), in terms of energy consumption and service tardiness. AGVS is an essential component in the modern flexible manufacturing complex for transporting materials and goods. High efficiency in the operation of the AGVS contributes to an increase in the stability of the whole manufacturing process, improving total production efficiency and profit. ABC and ACO algorithms are swarm intelligence methods based on the foraging behavior of honeybees and ants colonies in real-life. The proposed strategy is the combination of the local search ability of ACO and global search ability of ABC to avoid the local optimum trap of solutions. The approach demonstrates satisfactory performance based on different case studies in simulation for a large-scale AGV system, in comparison with other optimization methods. Results show that while the proposed method may not be the best in terms of the total travel distance, it allows for the safe travel of the AGV while still outperforming the original solutions in terms of time and energy cost.