Abstract
Assembly lines are at the core of many manufacturing systems, and planning for a well-balanced flow is key to ensure long-term efficiency. However, in flexible configurations such as Multi-Manned Assembly Lines (MMAL), the balancing problem also becomes more challenging. Due to the increased relevance of these assembly lines, this work aims to investigate the MMAL balancing problem, to contribute for a more effective decision-making process. Therefore, a new approach is proposed based on Deep Reinforcement Learning (DRL) embedded in a Digital Twin architecture. The proposed approach provides a close-to-reality training environment for the agent, using Discrete Event Simulation to simulate the production system dynamics. This methodology was tested on a real-world instance with preliminary results showing that similar solutions to the ones obtained using optimization-based strategies are achieved. This research provides evidence of success in terms of dynamic resource assignment to tasks and workers as a basis for future developments.

Abstract
The ability to describe business processes as executable models has always been one of the fundamental premises of workflow management. Yet, the tacit nature of human knowledge is often an obstacle to eliciting accurate process models. On the other hand, the result of process modeling is a static plan of action, which is difficult to adapt to changing procedures or to different business goals. In this article, we attempt to address these problems by approaching workflow management with a combination of learning and planning techniques. Assuming that processes cannot be fully described at buildtime, we make use of learning techniques, namely Inductive Logic Programming (ILP), in order to discover workflow activities and to describe them as planning operators. These operators will be subsequently fed to a partial-order planner in order to find the process model as a planning solution. The continuous interplay between learning, planning and execution aims at arriving at a feasible plan by successive refinement of the operators. The approach is illustrated in two simple scenarios. Following a discussion of related work, the paper concludes by presenting the main challenges that remain to be solved.

Abstract
This paper describes an approach towards workflow management based on the combination of learning and planning. Assuming that processes cannot be fully described at build-time, the approach makes use of learning techniques, namely Inductive Logic Programming (ILP), in order to discover workflow activities as planning operators. These operators will be subsequently fed to a partial-order planner in order to find the process model as a planning solution. The continuous interplay between learning, planning and execution aims at arriving at a feasible plan by successive refinement of the operators. The approach is illustrated in two simple scenarios. The paper concludes by relating the proposed approach with previous developments in this area.

Abstract
This paper proposes a new method to manage the use of ontologies in the context of Virtual Organisations Breeding Environments (VBEs). The research focuses on the dissolution phase of a Virtual Enterprise (VE), where ontology decomposition techniques are used to enrich the ontology library. First, an overview of the process used and the Ontology Library System (OLS) adopted are described. Then, the ontologies' ranking and classification method are described, explaining a set of metrics inspired by social network approaches. Finally, the composition method used to construct a global ontology and the decomposition algorithm implemented to segment an ontology are presented. Copyright © 2010 Inderscience Enterprises Ltd.

Abstract
This paper proposes a new method for managing the use of ontologies in the context of a Virtual Breeding Environment. This research work focus is on the dissolution phase of a Virtual Enterprise or Collaborative Network, where ontology segmentation technique are user to enrich the VBE's ontology libray. Firstly, an overview of the process of ontology composition and decomposition is given and the ontology library system adopted described. Then, the ontologies' ranking and classification method is described, explaining a set of metrics inspired in social network approaches. Finally, the results of preliminary tests are discussed.