An Effective Optimization Method for Integrated Scheduling of Multiple Automated Guided Vehicle Problems

Abstract

Automated Guided Vehicle (AGV) scheduling problem is an emerging research topic in the recent literature. This paper studies an integrated scheduling problem comprising task assignment and path planning for AGVs. To reduce the transportation cost of AGVs, this work also proposes an optimization method consisting of the total running distance, total delay time, and machine loss cost of AGVs. A mathematical model is formulated for the problem at hand, along with an improved Discrete Invasive Weed Optimization algorithm (DIWO). In the proposed DIWO algorithm, an insertion-based local search operator is developed to improve the local search ability of the algorithm. A staggered time departure heuristic is also proposed to reduce the number of AGV collisions in path planning. Comprehensive experiments are conducted, and 100 instances from actual factories have proven the effectiveness of the optimization method.