Self-organisation of mobile robots in large structure assembly using multi-agent systems

Competition between manufacturers in large structure assembly (LSA) is driven by the need to improve the adaptability and versatility of their manufacturing systems. The lack of these qualities in the currently used systems is caused by the dedicated nature of their fixtures and jigs. This has led to their underutilisation and costly changeover procedures. In addition to that, modern automation systems tend to be dedicated to very specific tasks. This means that such systems are highly specialised and can reach obsolescence once there is a substantial change in production requirements. In this doctoral thesis, a dynamic system consisting of mobile robots is proposed to overcome those limitations. As a first knowledge contribution in this doctoral thesis, it is investigated under which conditions using mobile robots instead of the traditional, fixed automation systems in LSA can be advantageous. In this context, dynamic systems are expected to be more versatile and adaptive than fixed systems. Unlike traditional, dedicated automation systems, they are not constrained to gantry rails or fixed to the floor. This results in an expanded working envelope and consequently the ability to reach more workstations. Furthermore, if a product is large enough, the manufacturer can choose how many mobile robots to deploy around it. Accordingly, it was shown that the ability to balance work rates on products and consequently meet their due times is improved. For the second knowledge contribution, two fundamentally different decision-making models for controlling mobile agents in the complex scheduling problem are investigated. This is done to investigate ways of taking full advantage from the potential benefits of applying mobile robots. It is found that existing models from related academic literature are not suited for the given problem. Therefore, two new models had to be proposed for this purpose. It was plausible to use an agent-based approach for self-organisation. This is because similarly to agents, mobile robots can perform independently of one-another; and have limited perception and communication abilities. Finally, through a comparison study, scenarios are identified where either model is better to use. In agreement with much of the established literature in the field, the models are shown to exhibit the common advantages and disadvantages of their respective architecture types. Considering that the enabling technologies are nearing sufficient maturity for deploying mobile robots in LSA, it is concluded that this approach can have several advantages. Firstly, the granularity and freedom of movement enables much more control over product completion times. Secondly, the increased working envelope enables higher utilisation of manufacturing resources. In the context of LSA, this is a considerable challenge because products take a very long time to get loaded and unloaded from workstations. However, if the product flow is steady, there are rare disruptions and rare production changes, fixed automation systems have an advantage due to requiring much less time (if any) for moving and localising. Therefore, mobile systems become more preferred to fixed systems in environments where there is an increasing frequency of disruptions and changes in production requirements. The validation of agent-based self-organisation models for mobile robots in LSA confirms the expectations based on existing literature. Also, it reveals that with relatively low amounts of spare capacity (5%) in the manufacturing systems, there is little need for sophisticated models. The value of optimised models becomes apparent when spare capacity approaches 0% (or even negative values) and there is less room for inefficiencies in scheduling

Decentralised vs partially centralised self-organisation model for mobile robots in large structure assembly

Currently, manufacturing companies are heavily investing into the automation of manufacturing processes. The push to improve productivity and efficiency is increasing the demand for more flexible and adaptable solutions than the currently common dedicated automation systems. In this paper, the planning problem for mobile robots in large structure assembly was addressed. Despite near-optimal results, the previously developed hybrid agent behaviour model was found to lack responsiveness and scalability. For that reason, an alternative, fully decentralised agent behaviour model was developed and compared to the hybrid one. Through simulated experiments, it was found that the decentralised agent behaviour model achieved much higher responsiveness; however, it required additional spare capacity to compensate for its decision-making imperfections

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Reconfiguring process plans: A mathematical programming approach

Increased global competition and frequent unpredictable market changes are current challenges facing manufacturing enterprises. Unpredictable changes of part design and engineering specifications trigger frequent and costly changes in process plans, which often require changes in the functionality and design of the manufacturing system. Process planning is a key logical enabler that should be further developed to cope with the changes encountered at the system level as well as to support the new manufacturing paradigms and continuously evolving products. Retrieval-based process planning predicated on rigid pre-defined boundaries of part families, does not satisfactorily support this changeable manufacturing environment. Since purely generative process planning systems are not yet a reality, a sequential hybrid approach at the macro-level has been proposed. Initially the master plan information of the part family\u27s composite part is retrieved, then modeling tools and algorithms are applied to arrive at the process plan of the new part, the definition of which does not necessarily lie entirely within the boundary of its original part family. Two distinct generative methods, namely Reconfigurable Process Planning (RPP) and Process Re-Planning were developed and compared. For RPP, a genuine reconfiguration of process plans to optimize the scope, extent and cost of reconfiguration is achieved using a novel 0-1 integer-programming model. Mathematical programming and formulation is proposed, for the first time, to reconfigure process plans to account for changes in parts\u27 features beyond the scope of the original product family. The computational time complexity of RPP is advantageously polynomial compared with the exponentially growing time complexity of its classical counterparts. As for Process Re-Planning, a novel adaptation of the Quadratic Assignment Problem (QAP) formulation has been developed, where machining features are assigned positions in one-dimensional space. A linearization of the quadratic model was performed. The proposed model cures the conceptual flaws in the classical Traveling Salesperson Problem; it also overcomes the complexity of the sub-tour elimination constraints and, for the first time, mathematically formulates the precedence constraints, which are a comer stone of the process planning problem. The developed methods, their limitations and merits are conceptually and computationally, analyzed, compared and validated using detailed industrial case studies. A reconfiguration metric on the part design level is suggested to capture the logical extent and implications of design changes on the product level; equally, on the process planning level a new criterion is introduced to evaluate and quantify impact of process plans reconfiguration on downstream shop floor activities. GAMS algebraic modeling language, its SBB mixed integer nonlinear programming solver, CPLEX solvers and Matlab are used. The presented innovative new concepts and novel formulations represent significant contributions to knowledge in the field of process planning. Their effectiveness and applicability were validated in different domains

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS