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The paper proposes an agent-based approach for measuring in real time energy consumption of resources in job-shop manufacturing processes. Data from industrial robots is collected, analysed and assigned to operation types, and then integrated in an optimization engine in order to estimate how alternating between makespan and energy consumption as objective functions affects the performances of the whole system. This study focuses on the optimization of energy consumption in manufacturing processes through operation scheduling on available resources. The decision making algorithm relies on a decentralized system collecting data about resources implementing thus an intelligent manufacturing control system; the optimization problem is implemented using IBM ILOG OPL.

Large scale emergence of mature cloud solutions, ranging from software-as-a-service based solutions for business management, to very sophisticate private cloud solutions; offer the building blocks for constructing extremely flexible enterprise systems that can respond to environmental changes with great agility. Manufacturing enterprises need to adopt these new technologies to advance in a new era of mass customization where flexibility, scalability and agility are the differentiating factors. In this context, this paper introduces the virtualized MES and shop floor architecture as an intermediate layer in the manufacturing stack and discusses the advantages offered by this approach for manufacturing enterprises. A classification of MES and shop floor devices is presented focusing on the virtualization techniques suitable for each device type, considering the level of distributed intelligence and the virtualization overhead. Shop floor virtualization through shop floor profiles is presented and discussed underlying the flexibility of the solution. A pilot multi-agent implementation for virtual shop floor configuration based on the CoBASA reference architecture is presented and discussed. The shop floor profiles which define the virtual layout and mappings of the robotized manufacturing system are also provided in this context. The pilot implementation using six Adapt robots and a IBM CloudBurst 2.1 private cloud, is described and virtualization overhead in terms of event propagation delays is measured and presented in several scenarios of resource workload collocation on physical cloud blades

This paper describes two heterarchical architectures which use distributed intelligence for the control (operation scheduling, resource allocation and product routing) of a manufacturing cell. In these architectures intelligence is put respectively at the product and through the network of workstation computers. The cost of embedded specialized hardware required for the first option is still very high. The second option needs the utilization of Automatic Identification and Data Capture technology close to the product to permit its identification and tracking by the computer system. These technologies enable the automated identification of objects, the collection of data about them, and the storage of that data directly into computer systems. Through this network, the computer system can access or update information in databases about the products and connect with software agents that represent production orders and resources and take in common decisions through negotiation and contract-based rules. The theory of the proposed solutions is based on the holonic manufacturing and product-driven automation concepts. The implementation methodology along with experimental results is presented, emphasizing the advantages of each solution.

Editorial of Special Issue; International audience; This special feature aims at shedding light on new emerging holonic and multi-agent systems operating in a service-and computing oriented manufacturing environment, using the latest ICT technologies such as service-orientation, mobile agents, Web-and Cloud services, virtualization, big data and analytics to name a few. Industrials are seeking for models and solutions that are not only able to provide efficient overall production performance, but also to face reactively a growing set of unpredicted events. The demand for large scale industrial systems running in complex and even chaotic environments requires the consideration of new paradigms and technologies that provide flexibility, robustness, agility and responsiveness. Holonic systems are, actually by definition, targeting challenges that include coping with the heterogeneous nature of manufacturing systems and their on-line interactive nature in combination with competitive pressures. Multi-agent systems is a suitable implementing approach to address these challenge by offering an alternative way to design control systems, based on the decentralization of control functions over distributed autonomous and cooperative entities. Moreover, virtualization of manufacturing execution system workloads offers a set of design and operational advantages to enterprises, the most visible being improved resource utilization and flexibility of the overall solution. At the manufacturing execution system level, cloud computing adoption refers mainly to virtualization of MES workloads. While MES implementations are different and usually depend directly on the actual physical shop floor layout, the general MES functions are aligned with the set of functions defined by ISA-95.03 specification. To achieve high levels of productivity growth and agility to market changes, manufacturers will need to leverage Big Data sets to drive efficiency across the networked enterprise. There is need for a framework allowing the development of manufacturing cyber physical systems that include capabilities for complex event processing and Big Data analytics, which are expected to move the manufacturing domain closer to digital transformation and cloud services within the contextual enterprise. On the other hand, service orientation is emerging at multiple organizational levels in enterprise business, and leverages technology in response to the growing need for greater business integration, flexibility and agility of manufacturing enterprises. Close related to IT infrastructures of Web Services, the Service Oriented Enterprise Architecture represents a technical architecture, a business modelling concept, an integration source and a new way of viewing units of control within the enterprise. Business and process information systems integration and interoperability are feasible by considering the customized product as "active controller" of the enterprise resources – thus providing consistency between material and informational flows. The areas of Service Oriented Computing and Multi-agent Systems are getting closer, both trying to deal with the same kind of environments formed by loose-coupled, flexible, persistent and distributed tasks. An example is the new approach of Service Oriented Multi-agent Systems (SoMAS).