## 1 Introduction  
The demand for more personalized, smart, and sustainable products has led to the rapid growth of the industrial Internet and cyber-physical technologies completely revolutionizing the manufacturing systems [1] The industrial world has switched from classical manufacturing systems to smart manufacturing systems based on self-contained, automated, and smart modules, exploiting flexibility and re-configurability capabilities. The Fourth Industrial Revolution (also known as Industry 4.0, Industrie 4.0, or I4.0) and related technologies have led to a significant evolution and transformation of the manufacturing systems. The key principles behind I4.0 are interconnection, information transparency, decentralized decisions, and real time technical assistance for human operators [2]. All parts of the manufacturing system should be connected with each other and have common data models. Manufacturing processes are therefore required to be significantly digitalized and simulation techniques are now paving the way to the 'digital factory' concept, a mix of virtual and real factory environments. Digital factory is defined as a \"comprehensive network of digital models, methods and tools--including modelling, simulation and 3D/virtual reality visualization--integrated by continuous data management\" [3] to simulate real manufacturing systems and optimize their performance. The digital factory applies production planning and control principles aimed at optimizing production systems to face more complex and competitive environments. In the I4.0 paradigm, modern manufacturing systems have to ensure re-configurability, flexibility, and intelligence. This result can be reached by more autonomous control systems. The Industry 4.0 paradigm emphasizes the development of Cyber-Physical Systems (CPS), digitalization, and interconnectivity. CPS are defined as an integration of resources from physical and cyber domains. In CPS, the computational resources from cyber world monitor the feedback from physical process hence dynamically computing the scenarios to generate control signals. By integration of new computational tools into manufacturing systems, CPSs have attained functionalities which are beneficial for enhancing productivity, quality, and reducing expenses. The communication capabilities were ensured by the Industrial CPS (ICPS), well-defined interfaces that enable CPS to integrate and communicate with the existing systems [4]. In this context, the traditional manufacturing production systems shift into smart production systems. Smart production systems are defined as fully integrated collaborative manufacturing systems that respond in real time to the changing demands and conditions of the factory, the supply network, and customer needs through digitalization of the manufacturing system [1]. These systems are characterized by advanced sensors, service-orientation, connected and autonomous CPS objects, collaborative supply networks, integrated and decentralized decision-making, interoperability, flexibility, and advanced analytics [5]. Advanced computational models are indeed required for addressing these capabilities [6]. These emerging architectural paradigms have to be transformed from static, rigid, and hierarchical into smart architectures composed and matched in a decentralized and dynamic fashion to accommodate the requirements of higher flexibility and reconfigurability [7]. In centralized control, a single component controls all the manufacturing entities. On the contrary, heterarchical control systems are customized and made up of several autonomous components able to communicate each other [8]. Switching from a centralized to a heterarchical (or decentralized) control can lead to a more efficient environment for production, especially in the case of deviations from ordinary working conditions [9]. This is why, nowadays, there is a growing interest in modular manufacturing systems and solutions supporting independent entities to communicate and operate autonomously, like multi-agent systems (MASs) [10].