Agent-based distributed manufacturing control: a state-of-the-art survey

Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re- usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future

Implementation and validation of a holonic manufacturing control system

Flexible manufacturing systems are complex, stochastic environments requiring the development of innovative, intelligent control architectures that support agility and re-configurability. ADACOR holonic control system addresses this challenge by introducing an adaptive production control approach supported by the presence of supervisor entities and the self-organization capabilities associated to each ADACOR holon. The validation of the concepts proposed by ADACOR control system requires their implementation and experimental testing, to analyze their correctness, applicability and merits. This paper describes the implementation of ADACOR concepts in a flexible manufacturing system, verifies their correctness and applicability, and evaluates the ADACOR control system performance, considering not only quantitative indicators directly related to production parameters, e.g. manufacturing lead time, but also qualitative indicators, such as the agility

Key Contributing Factors to the Acceptance of Agents in Industrial Environments

Multiple software agent-based solutions have been developed during the last decades, and applied with varying success to different domains offering control, reconfiguration, diagnosis, monitoring, etc. However, the promise that they once posed in terms of a new alternative decentralized approach offering modularity, flexibility and robustness, is only partially fulfilled. This paper investigates some key factors, i.e., design, technology, intelligence/algorithms, standardization, hardware, challenges, application and cost, which are hypothesized to be linked to the Industrial Agent acceptance. Empirical data was acquired via a conducted survey, and statistically analyzed to investigate the support of the posed hypotheses. The results indicate that all the factors are seen important issues that play a role toward deciding for or against an industrial agent solution.info:eu-repo/semantics/publishedVersio

Adaptive scheduling based on self-organized holonic swarm of schedulers

Scheduling plays an important role in the companies’ competiveness, dealing with complex combinatorial problems subject to uncertainty and emergence. In particular, in the ramp-up phase of small lot-sizes of complex products, scheduling is more demanding, e.g. due to late requests and immature technology products and processes. This paper presents the principles of a distributed scheduling architecture based on holonic and swarm principles and implemented using multi-agent system technology. In particular, it is described the coordination among the network of the swarm of schedulers and analysed the impact of embedded self-organization mechanisms.The research leading to these results has received funding from the European Union Seventh Framework Programme FP7 ARUM project, under grant agreement n° 314056.info:eu-repo/semantics/publishedVersio

Disturbance detection, recover and prediction in holonic manufacturing control

Indexado ao ISIDisturbance handling is a crucial issue in the development of intelligent and reconfigurable manufacturing control systems, supporting the fast and promptly response to the occurrence of unexpected disturbances. In holonic manufacturing control systems, the disturbance handling functions are distributed by the several autonomous control units. In this paper, a predictive disturbance handling approach is presented, transforming the traditional “fail and recover” practices into “predict and prevent” practices, allowing improving the control system performance

Agent-based holonic production control

Indexado ISIThe manufacturing system environment is typically a complex system, involving many variables and constraints, being in certain cases a chaotic system. The introduction of new paradigms to face globalisation, distribution of activities and customer satisfaction requirements, increases the problem complexity. The new manufacturing control approaches should support the agile adaptation to volatile technological and economical environments and should react dynamically and quickly to disturbances. This paper intends to introduce an agent-based approach to the manufacturing problem, that uses holonic concepts, is focused on distributed manufacturing shop floor control for discrete batch production, considers the optimisation of set-up and maintenance operations, and develops mechanisms for agile and fast reaction to disturbances without compromising the global production optimisation

Identification and characterization of iImprovement opportunities in industrial processes

Flexibility in responding to demand has become a major challenge for industries today. To achieve customer expectations, organizations must be flexible enough to offer a wide range of products and services that are available at any time and with the quality expected by customers. Digitization and Industry 4.0 have a strong impact on today's production environment. Established lean production methods are part of this flexibilization process and can be improved through new technologies. Any digitization must deal with waste and reduce it more effectively than a classic lean approach could. When compared to conventional automation, lean automation spaces are smaller, system prices are cheaper, inventory and energy use are lower. The system designer and operator, however, must have higher skills and knowledge. The integration of innovative automation technology along with lean production is an up-to-date and promising topic as industry 4.0 will not solve the problems of poorly organized and managed manufacturing systems. Furthermore, its tools must be applied to lean activities that are already successful even before automation.info:eu-repo/semantics/publishedVersio

Enhancing service-oriented holonic multi-agent systems with self-organization

Multi-agents systems and holonic manufacturing systems are suitable approaches to design a new and alternative class of production control systems, based on the decentralization of control functions over distributed autonomous and cooperative entities. However, in spite of their enormous potential they lack some aspects related to interoperability, migration, optimisation in decentralised structures and truly self-adaptation. This paper discusses the advantages of combining these paradigms with complementary paradigms, such as service-oriented architectures, and enhancing them with biologically inspired algorithms and techniques, such as emergent behaviour and self-organization, to reach a truly robust, agile and adaptive control system. An example of applying a stigmergy-based algorithm to dynamically route pallets in a production system is also provided

ADACOR: a holonic architecture for agile and adaptive manufacturing control

In the last decades significant changes in the manufacturing environment have been noticed: moving from a local economy towards a global economy, with markets asking for products with higher quality at lower costs, highly customised and with short life cycle. In these circumstances, the challenge is to develop manufacturing control systems with intelligence capabilities, fast adaptation to the environment changes and more robustness against the occurrence of disturbances. This paper presents an agile and adaptive manufacturing control architecture that addresses the need for the fast reaction to disturbances at the shop floor level, increasing the agility and flexibility of the enterprise, when it works in volatile environments. The proposed architecture introduces an adaptive control that balances dynamically between a more centralised structure and a more decentralised one, allowing combining the global production optimisation with agile reaction to unexpected disturbances

An agent-based disturbance handling architecture in manufacturing control

In industrial environments, disturbance handling is a major issue in reconfigurable manufacturing control systems, supporting the fast, effective and efficient response to the occurrence of unexpected disturbances. Those disturbances usually degrade the performance of the system, causing the loss of productivity and business opportunities, which are crucial roles to achieve competitiveness. This paper proposes an agent-based disturbance handling architecture that distributes the disturbance handling functions by several autonomous control units and considers the main types of shop floor disturbances that have impact at planning and scheduling level. The proposed architecture also integrates a prediction component, transforming the traditional “fail and recover” practices into “predict and prevent” practices