A novel multi-tasks chain scheduling algorithm based on capacity prediction to solve AGV dispatching problem in an intelligent manufacturing system

Abstract

With the rapid development of automated guided vehicle (AGV), the AGV dispatching problem (AGVDP) becomes a major concern in the AGV system owing to its decisive impact on the productivity of the modern industry. However, based on the instantaneous assignment scheduling strategy to arrange tasks for AGVs, most AGVDP researches prove to be incomplete for taking no account of the remaining capacity of AGVs. Therefore, intended to optimize the AGVDP, this paper presents a multi-tasks chain scheduling algorithm (MTCSA) through the fusion of multi-tasks chain model and capacity prediction model based on support vector machine, which is verified by a real intelligent manufacturing system. The results reveal that the makespan and heavy load ratio aided by MTCSA can be reduced and improved by a maximum of 20.0% and 8.5% to the studied manufacturing system respectively, and thus further confirm the superior performance of the presented algorithm.