Adaptive multi-agent system for a washing machine production line

This paper describes the implementation of a multi-agent system in a real industrial washing machine production line aiming to integrate process and quality control, allowing the establishment of feedback control loops to support adaptation facing condition changes. For this purpose, the agent-based solution was implemented using the JADE framework, being the shared knowledge structured using a proper ontology, edited and validated in Protégé and posteriorly integrated in the multi-agent system. The solution was intensively tested using historical real production data and it is now being installed in the real production line. The preliminary results confirm the initial expectations in terms of improvement of process performance and product quality

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Multi-agent system for integrating quality and process control in a home appliance production line

A current trend in manufacturing is the deployment of modular, distributed and intelligent control systems that introduce adaptation facing unexpected deviations and failures, namely in terms of production conditions and product demand fluctuation. The integration of quality and process control allows the implementation of dynamic self-adaptation procedures and feedback control loops to address a large variety of disturbances and changes in process parameters and variables, aiming to improve the production efficiency and the product quality. Multi-agent systems (MAS) technology (Wooldridge 2002)(Leitão et al. 2013) is suitable to face this challenge, offering an alternative way to design these adaptive systems, based on the decentralization of functions over distributed autonomous and cooperative agents, providing modularity, flexibility, adaptation and robustness. In spite of the potential benefits of the MAS technology, the number of deployed agent-based solutions in industrial environments, reported in the literature, are few, as illustrated in (Leitão et al. 2013) [colocar aqui referencia ao Pechoucek & Marik]. This chapter describes the development, installation and operation of a multi-agent system, designated as GRACE, integrating quality and process control to operate in a real home appliance production line, producing laundry washing machines, owned by Whirlpool and located in Naples, Italy. The use of the MAS technology acts as the intelligent and distributed infra-structure to support the implementation of real-time monitoring and feedback control loops that apply dynamic self-adaptation and optimization mechanisms to adjust the process and product variables. The agent-based solution was developed using the JADE (Java Agent DEvelopment Framework) framework and successfully installed in the industrial factory plant, contributing for demonstrating the effective applicability and benefits of the MAS technology, namely in terms of production efficiency and product quality.This work has been partly financed by the EU Commission, within the research contract GRACE coordinated by Univ. Politecnica delle Marche and having partners SINTEF, AEA srl, Instituto Politécnico de Bragança, Whirlpool Europe srl, Siemens AG.info:eu-repo/semantics/publishedVersio

Multiagent system integrating process and quality control in a factory producing laundry washing machines

Manufacturing companies are currently forced to reconsider their production processes by adopting more flexible, robust, and adaptive systems, aiming to improve their competitiveness. Multiagent systems (MASs) technology is suitable to address this challenge by providing an alternative way to design these complex systems based on the decentralization of the control functions over distributed entities. This paper describes the installation of a MAS solution in an industrial factory plant producing laundry washing machines. The installed solution focuses on the integration of quality and process control, and contributes to the maximization of the factory profitability facing changing conditions by applying self-adaptation procedures at the local and global levels. The preliminary results show improvements in the production efficiency and product quality, as well as a reduction of the scrap costs.info:eu-repo/semantics/publishedVersio

Intelligent products: the grace experience

Product intelligence is a new industrial manufacturing control paradigm aligned with the context of cyber-physical systems and addressing the current requirements of flexibility, reconfigurability and responsiveness. This paradigm introduces benefits in terms of improvement of the entire product׳s life-cycle, and particularly the product quality and customization, aiming the customer satisfaction. This paper presents an implementation of a system of intelligent products, developed under the scope of the GRACE project, where an agent-based solution was deployed in a factory plant producing laundry washing machines. The achieved results show an increase of the production and energy efficiency, an increase of the product quality and customization, as well as a reduction of the scrap costs.info:eu-repo/semantics/publishedVersio

Agent-based homeostatic control for green energy in the smart grid

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs

Multi-agent system for on-demand production integrating production and quality control

Multi-agent systems is being pointed as particularly suited to design and engineer a new class of control systems to operate at the factory plants addressing the current requirements of modularity, flexibility and re-configurability. This paper introduces the main principles of a multi-agent system approach to support the integration of production and quality control processes in washing machines production lines that is being developed under the EU FP7 GRACE project