Abstract
In the research of Automated Guided Vehicle (AGV) scheduling, the most critical issues are the optimization of task allocation to AGVs and the handling of conflict scenarios. To address these challenges, we propose a scheme for AGV scheduling optimization and conflict resolution. To begin with, we introduce a novel improved genetic algorithm grounded on a combination strategy that re-encodes tasks into compound groupings, effectively simplifying large-scale integer programing problems into smaller, more manageable ones. Subsequently, the simplified problem is solved using an improved genetic algorithm. Test results validate that this method not only quickens the pace of finding solutions but also significantly improves the quality of those solutions. This is particularly evident when it comes to managing larger-scale optimization challenges. Furthermore, within AGV system conflict scenarios, this paper divides them into two primary categories: navigational conflicts and task quantity changes. For navigational conflicts, three resolution approaches are designed to address four different types of conflict situations: head-on, crossing, occupation, and chasing conflicts. Considering the fluctuations in task quantity, we developed strategies for rescheduling, non-rescheduling, and insertion rescheduling. Their performances were experimentally compared across various scales of scheduling problems, providing data support and theoretical basis for the selection of scheduling strategies in practical applications.
Get full access to this article
View all access options for this article.