A multi-agent approach for design consistency checking

The last decade has seen an explosion of interest to advanced product development methods, such as Computer Integrated Manufacture, Extended Enterprise and Concurrent Engineering. As a result of the globalization and future distribution of design and manufacturing facilities, the cooperation amongst partners is becoming more challenging due to the fact that the design process tends to be sequential and requires communication networks for planning design activities and/or a great deal of travel to/from designers' workplaces. In a virtual environment, teams of designers work together and use the Internet/Intranet for communication. The design is a multi-disciplinary task that involves several stages. These stages include input data analysis, conceptual design, basic structural design, detail design, production design, manufacturing processes analysis, and documentation. As a result, the virtual team, normally, is very changeable in term of designers' participation. Moreover, the environment itself changes over time. This leads to a potential increase in the number of design. A methodology of Intelligent Distributed Mismatch Control (IDMC) is proposed to alleviate some of the related difficulties. This thesis looks at the Intelligent Distributed Mismatch Control, in the context of the European Aerospace Industry, and suggests a methodology for a conceptual framework based on a multi-agent architecture. This multi-agent architecture is a kernel of an Intelligent Distributed Mismatch Control System (IDMCS) that aims at ensuring that the overall design is consistent and acceptable to all participating partners. A Methodology of Intelligent Distributed Mismatch Control is introduced and successfully implemented to detect design mismatches in complex design environments. A description of the research models and methods for intelligent mismatch control, a taxonomy of design mismatches, and an investigation into potential applications, such as aerospace design, are presented. The Multi-agent framework for mismatch control is developed and described. Based on the methodology used for the IDMC application, a formal framework for a multi-agent system is developed. The Methods and Principles are trialed out using an Aerospace Distributed Design application, namely the design of an A340 wing box. The ontology of knowledge for agent-based Intelligent Distributed Mismatch Control System is introduced, as well as the distributed collaborative environment for consortium based projects

Decentralized Multi-Agent Production Control through Economic Model Bidding for Matrix Production Systems

Due to increasing demand for unique products, large variety in product portfolios and the associated rise in individualization, the efficient use of resources in traditional line production dwindles. One answer to these new challenges is the application of matrix-shaped layouts with multiple production cells, called Matrix Production Systems. The cycle time independence and redundancy of production cell capabilities within a Matrix Production System enable individual production paths per job for Flexible Mass Customisation. However, the increased degrees of freedom strengthen the need for reliable production control systems compared to traditional production systems such as line production. Beyond reliability a need for intelligent production within a smart factory in order to ensure goal-oriented production control under ever-changing manufacturing conditions can be ascertained. Learning-based methods can leverage condition-based reactions for goal-oriented production control. While centralized control performs well in single-objective situations, it is hard to achieve contradictory targets for individual products or resources. Hence, in order to master these challenges, a production control concept based on a decentralized multi-agent bidding system is presented. In this price-based model, individual production agents - jobs, production cells and transport system - interact based on an economic model and attempt to maximize monetary revenues. Evaluating the application of learning and priority-based control policies shows that decentralized multi-agent production control can outperform traditional approaches for certain control objectives. The introduction of decentralized multi-agent reinforcement learning systems is a starting point for further research in this area of intelligent production control within smart manufacturing

Інтелектуальні Агенти та Мультиагентні системи у виробництві

У статі досліджено інтелектуальні виробничі системи на основі інтелектуальних Агентів. Визначено відмінні характеристики та переваги Мультиагентної системи виробництва від класичної для виявлення нових можливостей підприємств. Штучний інтелект надає ряд переваг для підвищення продуктивності автоматичних складних систем, а Мультиагентні системи як парадигма, заснована на штучному інтелекті, підходить для задоволення поточних вимог, що пред'являються до промислових підприємств. Наведена класифікація існуючих інтелектуальних Агентів. Проаналізована модель розподіленої мережі інтелектуальної виробничої системи, що використовується у провідних світових компаніях та в економічних дослідженнях науковців, та базується на інтелектуальному Агенті.В статье исследованы интеллектуальные производственные системы на основе интеллектуальных Агентов. Определены отличительные характеристики и преимущества Мультиагентной системы производства от классической для выявления новых возможностей предприятий. Искусственный интеллект предоставляет ряд преимуществ для повышения производительности автоматических сложных систем, а Мультиагентные системы как парадигма, основанная на искусственном интеллекте, подходит для удовлетворения текущих требований, предъявляемых к промышленным предприятиям. Приведена классификация существующих интеллектуальных Агентов. Проанализирована модель распределенной сети интеллектуальной производственной системы, используемой в ведущих мировых компаниях и в экономических исследованиях ученых, и основана на интеллектуальном Агенте.The article investigates intelligent manufacturing systems based on intelligent Agents. The distinctive characteristics and advantages of the Multi-Agent manufacturing system from the classical to identify new opportunities for enterprises were determined. Artificial intelligence provides a number of advantages to increase productivity of complex automatic systems, and Multi-agent systems, as a paradigm based on artificial intelligence, are suitable for meeting current requirements for industrial enterprises. The classification of existing intelligent Agents is given. The model of distributed network of intelligent manufacturing system used in leading world companies and in scientist’s economic research analyzed, which based on an intelligent Agent

Multi-agent Manufacturing Execution System (MES):Concept, architecture & ML algorithm for a smart factory case

Smart factory of the future is expected to support interoperability on the shop floor, where information systems are pivotal in enabling interconnectivity between its physical assets. In this era of digital transformation, manufacturing execution system (MES) is emerging as a critical software tool to support production planning and control while accessing the shop floor data. However, application of MES as an enterprise information system still lacks the decision support capabilities on the shop floor. As an attempt to design intelligent MES, this paper demonstrates one of the artificial intelligence (AI) applications in the manufacturing domain by presenting a decision support mechanism for MES aimed at production coordination. Machine learning (ML) was used to develop an anomaly detection algorithm for multi-agent based MES to facilitate autonomous production execution and process optimization (in this paper switching the machine off after anomaly detection on the production line). Thus, MES executes the ‘turning off’ of the machine without human intervention. The contribution of the paper includes a concept of next-generation MES that has embedded AI, i.e., a MES system architecture combined with machine learning (ML) technique for multi-agent MES. Future research directions are also put forward in this position paper

Achieving cost competitiveness with an agent based integrated process planning and production scheduling system

From a manufacturing perspective, the efficiency of manufacturing operations (such as process planning and production scheduling) are the key element for enhancing manufacturing competence. Process planning and production scheduling functions have been traditionally treated as two separate activities, and have resulted in a range of inefficiencies. These include infeasible process plans, non-available/overloaded resources, high production costs, long production lead times, and so on. Above all, it is unlikely that the dynamic changes can be efficiently dealt with. Despite much research has been conducted to integrate process planning and production scheduling to generate optimised solutions to improve manufacturing efficiency, there is still a gap to achieve the competence required for the current global competitive market. In this research, the concept of multi-agent system (MAS) is adopted as a means to address the aforementioned gap. A MAS consists of a collection of intelligent autonomous agents able to solve complex problems. These agents possess their individual objectives and interact with each other to fulfil the global goal. This paper describes a novel use of an autonomous agent system to facilitate the integration of process planning and production scheduling functions to cope with unpredictable demands, in terms of uncertainties in product mix and demand pattern. The novelty lies with the currency-based iterative agent bidding mechanism to allow process planning and production scheduling options to be evaluated simultaneously, so as to search for an optimised, cost-effective solution. This agent based system aims to achieve manufacturing competence by means of enhancing the flexibility and agility of manufacturing enterprises

Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism

[EN] With the development of the market globalisation trend and increasing customer orientation, many uncertainties have entered into the manufacturing context. To create an agile response to the emergence of and change in conditions, this article presents a dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism. The dynamic re-scheduling function is the result of cooperation among several autonomous bio-inspired manufacturing cells with computing power and optimisation capabilities. The dynamic re-scheduling model is designed based on hormone regulation principles to agilely respond to the frequent occurrence of unexpected disturbances at the shop floor level. The cooperation mechanisms of the dynamic re-scheduling model are described in detail, and a test bed is set up to simulate and verify the dynamic re-scheduling approach. The results verify that the proposed method is able to improve the performances and enhance the stability of a manufacturing systemThis research was sponsored by the National Natural Science Foundation of China (NSFC) under Grant No. 51175262 and No. 61105114 and the Jiangsu Province Science Foundation for Excellent Youths under Grant BK20121011. This research was also sponsored by the CASES project supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under grant agreement No. 294931Zheng, K.; Tang, D.; Giret Boggino, AS.; Gu, W.; Wu, X. (2015). Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 229(S1):121-134. https://doi.org/10.1177/0954405414558699S121134229S1Maravelias, C. T., & Sung, C. (2009). Integration of production planning and scheduling: Overview, challenges and opportunities. Computers & Chemical Engineering, 33(12), 1919-1930. doi:10.1016/j.compchemeng.2009.06.007Yandra, & Tamura, H. (2007). A new multiobjective genetic algorithm with heterogeneous population for solving flowshop scheduling problems. International Journal of Computer Integrated Manufacturing, 20(5), 465-477. doi:10.1080/09511920601160288Fattahi, P., & Fallahi, A. (2010). Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability. CIRP Journal of Manufacturing Science and Technology, 2(2), 114-123. doi:10.1016/j.cirpj.2009.10.001Renna, P. (2011). Multi-agent based scheduling in manufacturing cells in a dynamic environment. International Journal of Production Research, 49(5), 1285-1301. doi:10.1080/00207543.2010.518736Qin, L., & Kan, S. (2013). Production Dynamic Scheduling Method Based on Improved Contract Net of Multi-agent. Advances in Intelligent Systems and Computing, 929-936. doi:10.1007/978-3-642-31656-2_128Iwamura, K., Mayumi, N., Tanimizu, Y., & Sugimura, N. (2010). A Study on Real-time Scheduling for Holonic Manufacturing Systems - Application of Reinforcement Learning -. Service Robotics and Mechatronics, 201-204. doi:10.1007/978-1-84882-694-6_35Jana, T. K., Bairagi, B., Paul, S., Sarkar, B., & Saha, J. (2013). Dynamic schedule execution in an agent based holonic manufacturing system. Journal of Manufacturing Systems, 32(4), 801-816. doi:10.1016/j.jmsy.2013.07.004Dan, Z., Cai, L., & Zheng, L. (2009). Improved multi-agent system for the vehicle routing problem with time windows. Tsinghua Science and Technology, 14(3), 407-412. doi:10.1016/s1007-0214(09)70058-6Hsieh, F.-S. (2009). Developing cooperation mechanism for multi-agent systems with Petri nets. Engineering Applications of Artificial Intelligence, 22(4-5), 616-627. doi:10.1016/j.engappai.2009.02.006Tang, D., Gu, W., Wang, L., & Zheng, K. (2011). A neuroendocrine-inspired approach for adaptive manufacturing system control. International Journal of Production Research, 49(5), 1255-1268. doi:10.1080/00207543.2010.518734Keenan, D. M., Licinio, J., & Veldhuis, J. D. (2001). A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis. Proceedings of the National Academy of Sciences, 98(7), 4028-4033. doi:10.1073/pnas.051624198Farhy, L. S. (2004). Modeling of Oscillations in Endocrine Networks with Feedback. Numerical Computer Methods, Part E, 54-81. doi:10.1016/s0076-6879(04)84005-9Cavalieri, S., Macchi, M., & Valckenaers, P. (2003). Journal of Intelligent Manufacturing, 14(1), 43-58. doi:10.1023/a:1022287212706Leitão, P., & Restivo, F. (2008). A holonic approach to dynamic manufacturing scheduling. Robotics and Computer-Integrated Manufacturing, 24(5), 625-634. doi:10.1016/j.rcim.2007.09.005Bal, M., & Hashemipour, M. (2009). Virtual factory approach for implementation of holonic control in industrial applications: A case study in die-casting industry. Robotics and Computer-Integrated Manufacturing, 25(3), 570-581. doi:10.1016/j.rcim.2008.03.020Leitao P. An agile and adaptive holonic architecture for manufacturing control. PhD Thesis, University of Porto, Porto, 2004

Towards self-adaptable intelligent assembly systems

Currently, European small and medium-sized enterprises (SMEs) are experiencing increasing pressure to provide high quality goods with customised features while at the same time remain cost effective and competitive in the global market. In the future, manufacturing systems need to be able to cope with constantly changing market requirements. Consequently, there is a need to develop the research foundations for a new generation of manufacturing systems composed of intelligent autonomous entities which are able to reconfigure themselves and to adapt their performance as a result of product and environmental changes. The research described in this thesis addresses the issue by developing three distinctive elements of an adaptation framework for next-generation manufacturing systems. The first element is a capability-based data model for the representation of manufacturing resources to enable self-awareness. The model captures the resources’ life cycle and performance indicators to provide information about the resources’ condition. The second element is a multi-agent architecture for plug and produce and the reconfiguration of manufacturing systems. The resource data model is utilised by the agent society, which is able to instantiate a model to represent a physical resource in the virtual agent society. The shift to the virtual environment enables a communication infrastructure for heterogeneous resources and the application of the digital twin concept. The agent architecture applies negotiation techniques to establish a plan for system adaptation. The third element is a methodology for automated experience-based manufacturing system adaptation. The adaptation methodology is based on previous runtime experience instances to generate adaptation knowledge. The information generated is applied to the current context and part of the agent negotiation which is dynamically executed in case of a disturbance. Collectively, these three elements significantly increase the flexibility and reconfigurability of a manufacturing system reducing the time required for integration and maintenance of complex systems on demand, improving their effectiveness. The developed framework is implemented and evaluated experimentally on a physical, industrial standard demonstrator and using a virtual simulation model. The experimental results confirm a significant step towards new solutions for the deployment of self-adaptable intelligent manufacturing systems

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

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

Scheduling of non-repetitive lean manufacturing systems under uncertainty using intelligent agent simulation

World-class manufacturing paradigms emerge from specific types of manufacturing systems with which they remain associated until they are obsolete. Since its introduction the lean paradigm is almost exclusively implemented in repetitive manufacturing systems employing flow-shop layout configurations. Due to its inherent complexity and combinatorial nature, scheduling is one application domain whereby the implementation of manufacturing philosophies and best practices is particularly challenging. The study of the limited reported attempts to extend leanness into the scheduling of non-repetitive manufacturing systems with functional shop-floor configurations confirms that these works have adopted a similar approach which aims to transform the system mainly through reconfiguration in order to increase the degree of manufacturing repetitiveness and thus facilitate the adoption of leanness. This research proposes the use of leading edge intelligent agent simulation to extend the lean principles and techniques to the scheduling of non-repetitive production environments with functional layouts and no prior reconfiguration of any form. The simulated system is a dynamic job-shop with stochastic order arrivals and processing times operating under a variety of dispatching rules. The modelled job-shop is subject to uncertainty expressed in the form of high priority orders unexpectedly arriving at the system, order cancellations and machine breakdowns. The effect of the various forms of the stochastic disruptions considered in this study on system performance prior and post the introduction of leanness is analysed in terms of a number of time, due date and work-in-progress related performance metrics