# An Event-Driven Agent-Based Simulation Model for Industrial Processes
## 1 Introduction  
The terms \"process manufacturing industry\" or \"process industry\" usually refer to the industrial sector, which transform primary raw materials into products that might afterwards be further processed and transformed by other industries in order to deliver complex assembled products [1]. It includes, for instance, the metallurgical, chemical, glass, pulp and paper and food industries. Each manufacturing process contains several steps, starting from storage, transportation and preparation of primary raw materials, melting and/or transformation into chemical reactors, refining, shaping (e.g., casting, hot and cold rolling) and eventually, coating. All these sub-processes are closely correlated and often seamlessly connected. In effect, many of these production cycles are at least partly continuous, i.e., the production runs 24/7. During the different stages, unexpected events may happen causing e.g., delays, waste of materials and off-spec products, thus, the overall engineering system must rapidly and efficiently react in order to mitigate the effects of such events. With the paradigm of Industry 4.0, all the industrial sectorsare keen to implement the concept of a \"smart factory\", that is a context-sensitive manufacturing environment that can handle turbulences in real-time production using decentralized information and communication structures for an optimum management of production processes [2]. A paradigm very suited for addressing smart and distributed systems acting in complex and dynamic environments like process industries is the agent-based approach. A Multi-Agent System (MAS) consists of several computational entities capable of independent actions, called agents, which communicate with each other, and have the ability to cooperate, coordinate, and negotiate [3]. The research on agent-based approaches continues to provide technological solutions in a wide variety of domains [4], especially the use of agent abstraction for modeling and simulating systems. In reference [5], for instance, a multi-agent simulation was used to evaluate the traders' behavior in perishable goods markets under several market conditions, whereas an agent-based model for the management of traffic flows in a network under distinctive congestion scenarios was presented in reference [6]. Concerning manufacturing applications, the potential of MAS is very promising and the agent-based paradigm for modeling and simulation has receiving ever increasing attention during the last two decades. The leading role of MAS as fundamental technology for industrial production management and optimization in light of current and future developments of the digitalization of industrial production cycles is highlighted in reference [7]. A survey of agent applications in the manufacturing domain, ranging from engineering design to life-cycle management, was presented in reference [8], whereas a discussion of the advantages of using agent-based modeling tools to support the development of agent-based manufacturing control systems was presented in reference [9]. In reference [10], agent-based modeling and simulation were used for the design and verification of an automatic manufacturing execution system with the aim of evaluating emerging behaviors and macroscopic dynamics in a multiproduct batch plant. An agent-based assembly system simulation was presented in reference [11] that shows how agents handle a production plan dynamically using communication and negotiation. In reference [12], one of the first examples of application of MAS in the steel sector was proposed, which targets the dynamic reallocation of products. MAS were also successfully applied in electric steelworks for dynamic energy management [13; 14]. More recently, in reference [15], an on-line and distributed scheduling system based on multi-agent systems was proposed in order to manage both the scheduling of machines and the automated guided vehicles of a manufacturing system.