Optimising the maintenance strategy for a multi-AGV system using genetic algorithms

Automated Guided Vehicles (AGVs) are playing increasingly vital roles in a variety of applications in modern society, such as intelligent transportation in warehouses and material distribution in automated production lines. They improve production efficiency, save labour cost, and bring significant economic benefit to end users. However, to utilise these potential benefits is highly dependent on the reliability and availability of the AGVs. In other words, an effective maintenance strategy is critical in the application of AGVs. The research activity reported in this paper is to realise an effective maintenance strategy for a multi-AGV system by the approach of Genetic Algorithms (GA). To facilitate the research, an automated material distribution system consisting of three AGVs is considered in this paper for methodology development. The movement of every AGV in the multi-AGV system, and the corrective and periodic preventive maintenances of failed AGVs are modelled using the approach of Coloured Petri Nets (CPNs). Then, a GA is adopted for optimising the maintenance and associated design and operation of the multi-AGV system. From this research, it is disclosed that both the location selection of the maintenance site and the maintenance strategies that are adopted for AGV maintenance have significant influences on the efficiency, cost, and productivity of a multi-AGV system

Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda

Autonomous mobile robots (AMR) are currently being introduced in many intralogistics operations, like manufacturing, warehousing, cross-docks, terminals, and hospitals. Their advanced hardware and control software allow autonomous operations in dynamic environments. Compared to an automated guided vehicle (AGV) system in which a central unit takes control of scheduling, routing, and dispatching decisions for all AGVs, AMRs can communicate and negotiate independently with other resources like machines and systems and thus decentralize the decision-making process. Decentralized decision-making allows the system to react dynamically to changes in the system state and environment. These developments have influenced the traditional methods and decision-making processes for planning and control. This study identifies and classifies research related to the planning and control of AMRs in intralogistics. We provide an extended literature review that highlights how AMR technological advances affect planning and control decisions. We contribute to the literature by introducing an AMR planning and control framework t

Control of free-ranging automated guided vehicles in container terminals

Container terminal automation has come to the fore during the last 20 years to improve their efficiency. Whereas a high level of automation has already been achieved in vertical handling operations (stacking cranes), horizontal container transport still has disincentives to the adoption of automated guided vehicles (AGVs) due to a high degree of operational complexity of vehicles. This feature has led to the employment of simple AGV control techniques while hindering the vehicles to utilise their maximum operational capability. In AGV dispatching, vehicles cannot amend ongoing delivery assignments although they have yet to receive the corresponding containers. Therefore, better AGV allocation plans would be discarded that can only be achieved by task reassignment. Also, because of the adoption of predetermined guide paths, AGVs are forced to deploy a highly limited range of their movement abilities while increasing required travel distances for handling container delivery jobs. To handle the two main issues, an AGV dispatching model and a fleet trajectory planning algorithm are proposed. The dispatcher achieves job assignment flexibility by allowing AGVs towards to container origins to abandon their current duty and receive new tasks. The trajectory planner advances Dubins curves to suggest diverse optional paths per origin-destination pair. It also amends vehicular acceleration rates for resolving conflicts between AGVs. In both of the models, the framework of simulated annealing was applied to resolve inherent time complexity. To test and evaluate the sophisticated AGV control models for vehicle dispatching and fleet trajectory planning, a bespoke simulation model is also proposed. A series of simulation tests were performed based on a real container terminal with several performance indicators, and it is identified that the presented dispatcher outperforms conventional vehicle dispatching heuristics in AGV arrival delay time and setup travel time, and the fleet trajectory planner can suggest shorter paths than the corresponding Manhattan distances, especially with fewer AGVs.Open Acces