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

Enhancing the performance of automated guided vehicles through reliability, operation and maintenance assessment

Automated guided vehicles (AGVs), a type of unmanned moving robots that move along fixed routes or are directed by laser navigation systems, are increasingly used in modern society to improve efficiency and lower the cost of production. A fleet of AGVs operate together to form a fully automatic transport system, which is known as an AGV system. To date, their added value in efficiency improvement and cost reduction has been sufficiently explored via conducting in-depth research on route optimisation, system layout configuration, and traffic control. However, their safe application has not received sufficient attention although the failure of AGVs may significantly impact the operation and efficiency of the entire system. This issue becomes more markable today particularly in the light of the fact that the size of AGV systems is becoming much larger and their operating environment is becoming more complex than ever before. This motivates the research into AGV reliability, availability and maintenance issues in this thesis, which aims to answer the following four fundamental questions: (1) How could AGVs fail? (2) How is the reliability of individual AGVs in the system assessed? (3) How does a failed AGV affect the operation of the other AGVs and the performance of the whole system? (4) How can an optimal maintenance strategy for AGV systems be achieved? In order to answer these questions, the method for identifying the critical subsystems and actions of AGVs is studied first in this thesis. Then based on the research results, mathematical models are developed in Python to simulate AGV systems and assess their performance in different scenarios. In the research of this thesis, Failure Mode, Effects and Criticality Analysis (FMECA) was adopted first to analyse the failure modes and effects of individual AGV subsystems. The interactions of these subsystems were studied via performing Fault Tree Analysis (FTA). Then, a mathematical model was developed to simulate the operation of a single AGV with the aid of Petri Nets (PNs). Since most existing AGV systems in modern industries and warehouses consist of multiple AGVs that operate synchronously to perform specific tasks, it is necessary to investigate the interactions between different AGVs in the same system. To facilitate the research of multi-AGV systems, the model of a three-AGV system with unidirectional paths was considered. In the model, an advanced concept PN, namely Coloured Petri Net (CPN), was creatively used to describe the movements of the AGVs. Attributing to the application of CPN, not only the movements of the AGVs but also the various operation and maintenance activities of the AGV systems (for example, item delivery, corrective maintenance, periodic maintenance, etc.) can be readily simulated. Such a unique technique provides us with an effective tool to investigate larger-scale AGV systems. To investigate the reliability, efficiency and maintenance of dynamic AGV systems which consist of multiple single-load and multi-load AGVs traveling along different bidirectional routes in different missions, an AGV system consisting of 9 stations was simulated using the CPN methods. Moreover, the automatic recycling of failed AGVs is studied as well in order to further reduce human participation in the operation of AGV systems. Finally, the simulation results were used to optimise the design, operation and maintenance of multi-AGV systems with the consideration of the throughputs and corresponding costs of them.The research reported in this thesis contributes to the design, reliability, operation, and maintenance of large-scale AGV systems in the modern and rapidly changing world.</div

A petri-net based methodology for modeling, simulation, and control of flexible manufacturing systems

Global competition has made it necessary for manufacturers to introduce such advanced technologies as flexible and agile manufacturing, intelligent automation, and computer-integrated manufacturing. However, the application extent of these technologies varies from industry to industry and has met various degrees of success. One critical barrier leading to successful implementation of advanced manufacturing systems is the ever-increasing complexity in their modeling, analysis, simulation, and control. The purpose of this work is to introduce a set of Petri net-based tools and methods to address a variety of problems associated with the design and implementation of flexible manufacturing systems (FMSs). More specifically, this work proposes Petri nets as an integrated tool for modeling, simulation, and control of flexible manufacturing systems (FMSs). The contributions of this work are multifold. First, it demonstrates a new application of PNs for simulation by evaluating the performance of pull and push diagrams in manufacturing systems. Second, it introduces a class of PNs, Augmented-timed Petri nets (ATPNs) in order to increase the power of PNs to simulate and control flexible systems with breakdowns. Third, it proposes a new class of PNs called Realtime Petri nets (RTPNs) for discrete event control of FMS s. The detailed comparison between RTPNs and traditional discrete event methods such as ladder logic diagrams is presented to answer the basic question \u27Why is a PN better tool than ladder logic diagram?\u27 and to justify the PN method. Also, a conversion procedure that automatically generates PN models from a given class of logic control specifications is presented. Finally, a methodology that uses PNs for the development of object-oriented control software is proposed. The present work extends the PN state-of-the-art in two ways. First, it offers a wide scope for engineers and managers who are responsible for the design and the implementation of modem manufacturing systems to evaluate Petri nets for applications in their work. Second, it further develops Petri net-based methods for discrete event control of manufacturing systems

Intelligent Simulation Modeling of a Flexible Manufacturing System with Automated Guided Vehicles

Although simulation is a very flexible and cost effective problem solving technique, it has been traditionally limited to building models which are merely descriptive of the system under study. Relatively new approaches combine improvement heuristics and artificial intelligence with simulation to provide prescriptive power in simulation modeling. This study demonstrates the synergy obtained by bringing together the "learning automata theory" and simulation analysis. Intelligent objects are embedded in the simulation model of a Flexible Manufacturing System (FMS), in which Automated Guided Vehicles (AGVs) serve as the material handling system between four unique workcenters. The objective of the study is to find satisfactory AGV routing patterns along available paths to minimize the mean time spent by different kinds of parts in the system. System parameters such as different part routing and processing time requirements, arrivals distribution, number of palettes, available paths between workcenters, number and speed of AGVs can be defined by the user. The network of learning automata acts as the decision maker driving the simulation, and the FMS model acts as the training environment for the automata network; providing realistic, yet cost-effective and risk-free feedback. Object oriented design and implementation of the simulation model with a process oriented world view, graphical animation and visually interactive simulation (using GUI objects such as windows, menus, dialog boxes; mouse sensitive dynamic automaton trace charts and dynamic graphical statistical monitoring) are other issues dealt with in the study

Automated guided vehicle mission reliability modelling using a combined fault tree and Petri net approach

Automated guided vehicles (AGVs) are being extensively used for intelligent transportation and distribution of materials in warehouses and autoproduction lines due to their attributes of high efficiency and low costs. Such vehicles travel along a predefined route to deliver desired tasks without the supervision of an operator. Much effort in this area has focused primarily on route optimisation and traffic management of these AGVs. However, the health management of these vehicles and their optimal mission configuration have received little attention. To assure their added value, taking a typical AGV transport system as an example, the capability to evaluate reliability issues in AGVs are investigated in this paper. Following a failure modes effects and criticality analysis (FMECA), the reliability of the AGV system is analysed via fault tree analysis (FTA) and the vehicles mission reliability is evaluated using the Petri net (PN) method. By performing the analysis, the acceptability of failure of the mission can be analysed, and hence the service capability and potential profit of the AGV system can be reviewed and the mission altered where performance is unacceptable. The PN method could easily be extended to have the capability to deal with fleet AGV mission reliability assessment

Towards the Exhaustive Verification of Real-Time Aspects in Controller Implementation

In industrial applications, the number of final products endowed with real-time automatic control systems that manage critical situations as far as human safety is concerned has dramatically increased. Thus, it is of growing importance that the control system design flow encompasses also its translation into software code and its embedding into a hardware and software network. In this paper, a tool-supported approach to the formal analysis of real-time aspects in controller implementation is proposed. The analysis can ensure that some desired properties of the control loop are preserved in its implementation on a distributed architecture. Moreover, the information extracted automatically from the model can also be used to approach straightforwardly some design problems, such as the hardwar

Search-based system architecture development using a holistic modeling approach

This dissertation presents an innovative approach to system architecting where search algorithms are used to explore design trade space for good architecture alternatives. Such an approach is achieved by integrating certain model construction, alternative generation, simulation, and assessment processes into a coherent and automated framework. This framework is facilitated by a holistic modeling approach that combines the capabilities of Object Process Methodology (OPM), Colored Petri Net (CPN), and feature model. The resultant holistic model can not only capture the structural, behavioral, and dynamic aspects of a system, allowing simulation and strong analysis methods to be applied, it can also specify the architectural design space. Both object-oriented analysis and design (OOA/D) and domain engineering were exploited to capture design variables and their domains and define architecture generation operations. A fully realized framework (with genetic algorithms as the search algorithm) was developed. Both the proposed framework and its suggested implementation, including the proposed holistic modeling approach and architecture alternative generation operations, are generic. They are targeted at systems that can be specified using object-oriented or process-oriented paradigm. The broad applicability of the proposed approach is demonstrated on two examples. One is the configuration of reconfigurable manufacturing systems (RMSs) under multi-objective optimization and the other is the architecture design of a manned lunar landing system for the Apollo program. The test results show that the proposed approach can cover a huge number of architecture alternatives and support the assessment of several performance measures. A set of quality results was obtained after running the optimization algorithm following the proposed framework --Abstract, page iii