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

Applying a reconfigurable multi-agent scheduler to product distribution

More intelligent scheduling methods are required for manufacturing scheduling due to the move to more agile systems. Multi-agent methods are one such approach. This paper describes the application of a reconfigurable multi-agent scheduler to the problem of allocating orders to warehouses in a distribution supply chain. This multi-agent system was originally developed for allocation of orders to machines in a highly reconfigurable manufacturing system and this work was aimed at investigating the ease of applying this same scheduler to other problems. It was found that this new application was readily achieved because of the modular structure of the scheduler. This paper shows how the application to the new problem was achieved. <br /

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

Multi-agent Manufacturing Execution System (MES):Concept, architecture &amp; 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

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

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

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

A distributed multi-agent framework for shared resources scheduling

Nowadays, manufacturers have to share some of their resources with partners due to the competitive economic environment. The management of the availability periods of shared resources causes a problem because it is achieved by the scheduling systems which assume a local environment where all resources are on the same site. Therefore, distributed scheduling with shared resources is an important research topic in recent years. In this communication, we introduce the architecture and behavior of DSCEP framework (distributed, supervisor, customer, environment, and producer) under shared resources situation with disturbances. We are using a simple example of manufacturing system to illustrate the ability of DSCEP framework to solve the shared resources scheduling problem in complex systems

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems