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Cooperative Control in Production and Logistics*

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Cooperative Control in Production and Logistics* Cooperative Control in Production and Logistics* László Monostori1,2,**, Paul Valckenaers3, Alexandre Dolgui4, Hervé Panetto5, Mietek Brdys6,7, Balázs Csanád Csáji1 1 Institute for Computer Science and Control, Hungarian Academy of Sciences, Hungary 2 Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Hungary 3 Department of Health Care and Technology, KHLeuven, Belgium 4 LIMOS UMR 6158 CNRS, Ecole Nationale Supérieure des Mines de Saint-Etienne, France 5 Centre de Recherche en Automatique de Nancy (UMR 7039), Université de Lorraine, CNRS, France 6 School of Electronic, Electrical and Computer Engineering, University of Birmingham, United Kingdom 7Department of Control Systems Engineering, Gdansk University of Technology, Gdansk, Poland Abstract: Classical applications of control engineering and information and communication technology (ICT) in production and logistics are often done in a rigid, centralized and hierarchical way. These inflex- ible approaches are typically not able to cope with the complexities of the manufacturing environment, such as the instabilities, uncertainties and abrupt changes caused by internal and external disturbances, or a large number and variety of interacting, interdependent elements. A paradigm shift, e.g., novel organiz- ing principles and methods, is needed for supporting the interoperability of dynamic alliances of agile and networked systems. Several solution proposals argue that the future of manufacturing and logistics lies in network-like, dynamic, open and reconfigurable systems of cooperative autonomous entities. The paper overviews various distributed approaches and technologies of control engineering and ICT that can support the realization of cooperative structures from the resource level to the level of networked en- terprises. Standard results as well as recent advances from control theory, through cooperative game the- ory, distributed machine learning to holonic systems, cooperative enterprise modelling, system integra- tion, and autonomous logistics processes are surveyed. A special emphasis is put on the theoretical de- velopments and industrial applications of Robustly Feasible Model Predictive Control (RFMPC). Two case studies are also discussed: i) a holonic, PROSA-based approach to generate short-term forecasts for an additive manufacturing system by means of a delegate multi-agent system (D-MAS); and ii) an appli- cation of distributed RFMPC to a drinking water distribution system. Keywords: intelligent manufacturing systems, complex systems, agents, production control, distributed control, predictive control, adaptive control, machine learning, optimization 1. INTRODUCTION 2008; ElMaraghy, et al., 2012). Decision should be based on the pertinent information; time should be seriously consid- The development in control engineering and information and ered as a limiting resource for decision-making, and the pro- communication technology (ICT) always acted as important duction and logistics systems should have changeable, easy- enablers for newer and newer solutions – moreover genera- to-reconfigure organizational structures. tions – in production and logistics. New organizing principles and methods are needed for sup- As to discrete manufacturing, developments in ICT led to the porting the interoperability of dynamic virtual alliances of realization of product life-cycle management (PLM), com- agile and networked systems which – when acting together – puter numerical control (CNC), enterprise resource planning can make use of opportunities without suffering from dise- (ERP) and computer integrated manufacturing (CIM) sys- conomies of scale (Monostori, et al., 2006). tems. Integration often resulted in rigid, centralized or hierar- chical control architectures which could not cope with an Various solution proposals unanimously imply that the future unstable and uncertain manufacturing environment: internal of manufacturing and logistics lies in the loose and temporal as well as external disturbances in manufacturing and related federations of cooperative autonomous entities (Vámos, logistics and frequently changing market demands. 1983). The interaction of individuals may lead to emergence of complex system-level behaviors (Ueda et al., 2001). Evo- Growing complexity is another feature showing up in produc- lutionary system design relies on this emergence when mod- tion and logistics processes, furthermore, in enterprise struc- elling and analyzing complex manufacturing and logistics in tures, as well (Wiendahl and Scholtissek, 1994; Schuh, et al., a wider context of eco-technical systems. * Status Report prepared by the IFAC Coordinating Committee on Manufacturing and Logistics Systems. ** Corresponding author. Tel.: +36 1 2796159, fax: +36 1 4667503, email address: [email protected] Under the pressure of the challenges highlighted above, the Global performance Local autonomy transformations of manufacturing and logistics systems are Cooperation Competition already underway (Jovane et al., 2003). The need for novel organizational principles, structures and method has called Information sharing Private data, security for various approaches (Tharumarajah, et al., 1996) in the Communication Truthfulness, reputation past decades, such as holonic (Van Brussel, et al., 1998; Adaptivity Network stability Valckenaers and Van Brussel, 2005), fractal (Warnecke, Responsiveness Economic production 1993), random (Iwata et al., 1994), biological (Ueda, et al., 1997), multi-agent manufacturing systems (Bussmann, et al., Optimization Robustness 2004; Monostori, et al., 2006), bucket brigades (Bartholdi Expected behaviour Emergent properties and Eisenstein, 1996; Bratcu & Dolgui, 2005; Dolgui and Mass production’s Customization Proth, 2010), and autonomous logistics systems (Scholz- efficiency Reiter and Freitag, 2007). Fig. 2. Compelling challenges of cooperative production and In a milestone paper (Nof, et al., 2006) – based on the scopes, logistic systems. activities and results of all the Technical Committees (TCs) of the Coordinating Committee on Manufacturing and Logis- Advantages (e.g., why should cooperative control approaches tics Systems (CC5) of the International Federation of Auto- be used in production and logistics) include matic Control (IFAC) – four emerging trends for solution approaches were identified (Figure 1). Openness (e.g., easier to build and change) Reliability (e.g., fault tolerance) Performance (e.g., distributed execution of tasks) Integration, Scope of functional E-Work, Scalability (e.g., the potential of addressing large-scale Coordination, e-Mfg, problems, incremental design) challenges and solutions Collaborative & e-Logistics Control Flexibility (e.g., redesign, heterogeneity) Manufacturing Cost (e.g., potential cost reductions) Manufacturing Modeling for Plant & Logistics Distribution (e.g., natural for spatially separated units) Management & Distributed Automation Active Control Decision Technologies While some disadvantages of cooperative control systems, Networks which need to be addressed, are as follows Enterprise Large Scale Communication Overhead (e.g., time / cost of sharing Integration & Complex Scope of emerging trends for information) Networking Systems solution approaches Decentralized Information (e.g., local vs global data) Security / Confidentiality (are harder to guarantee) Fig. 1. Scope of functional challenges/solutions and emerging Decision “Myopia” (e.g., local optima) trends for solution approaches (Nof, et al., 2006). Chaotic Behavior (e.g., butterfly effects, bottlenecks) Complex to Analyze (compared to centralized systems) The aforementioned milestone paper, concentrating on e- work, e-manufacturing and e-logistics enabled by the Inter- The main aim of the paper is to highlight how distributed net, underlined the importance of understanding how to mod- control approaches can contribute at least to partially reduce el, design and control effective e-work, in order to secure the the disadvantages while using completely the advantages, i.e. productivity and competitiveness of manufacturing and logis- to find a safe – sometimes even narrow – path in between two tics systems. extremes (only advantages or disadvantages). In addition to cooperativeness, another indispensable charac- Another goal of the paper is to survey distributed methods of teristic of production and logistics systems of the future, control theory and ICT which can support the realization of namely responsiveness, was underlined in (Váncza et al., cooperative structures from the resource level to the level of 2011) where the concept of cooperative and responsive man- networked enterprises (top right circle of Figure1). ufacturing enterprises (CoRMEs) was introduced and the The structure of the paper is as follows. Section 2 overviews heavy challenges in their realization were emphasized togeth- a few relevant concepts from the theory of distributed-, multi- er with some possible resolutions of them. agent-based and cooperative control systems. Section 3 aims Applications of cooperative control approaches – as in many at discussing recent approaches to Robustly Feasible Model fields – raise real, difficult to answer challenges in manufac- Predictive Control, which is one of the highlights of the pa- turing and logistics. These challenges – see, for example, per. Later, Section 4 presents some existing paradigms and Figure 2 – are heavy because they are directly stemming from specialized
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