MLATSO: A method for task scheduling optimization in multi-load AGVs-based systems

In the context of increasingly competitive intelligent manufacturing, the multi-load Automated guided vehicles (AGVs) based Automated Storage and Retrieval System (AS/RS) has been of particular interest, as reductions in the number of AGVs required can significantly decrease potential congestions and increase the system effectiveness. In comparison with the single-load AGVs system, more difficult and critical issue of scheduling multi-load AGVs to automate storage/retrieval missions and to maximize economic benefits remains unresolved. Therefore, we propose a task scheduling optimization method for multi-load AGVs-based systems, with which, the objectives of least number of occupied AGVs, shortest travel time and minimum conflicts can be met simultaneously. The experiments are conducted in various scenarios, and verify that our work can use fewer AGVs to optimize tasks delivery, which enables the AS/RS stakeholders to reach win-win results for system performance and AGVs investment, thus maximizing economic benefit.

Automated summary

In the context of competitive intelligent manufacturing, the scheduling optimization of multi-load AGVs-based systems is crucial for automating storage/retrieval tasks and maximizing economic benefits. Our proposed method aims to simultaneously achieve the objectives of minimizing occupied AGVs, reducing travel time, and minimizing conflicts. Experiment results demonstrate that our method can optimize task delivery using fewer AGVs, leading to win-win results for system performance and AGVs investment, thus maximizing economic benefit.