## 3 A System Architecture for Reconfigurable Manufacturing System Based on CPS Paradigm  
A reconfigurable manufacturing system described by a Cyber-Physical System in IoT-enabled environment consists of two parts, the Physical World and the Cyber World. Figure 1 shows the Physical World of RMS. It consists of the entities of workers, machines and parts in the system. The Cyber World of RMS is described by the models that represent resources such as workers, machines, parts, processes and data relevant to the decision making and operations of RMS. Figure 2 shows a Cyber World associated with Figure 1. In Figure 2, the entities of RMS in the Cyber World are modeled by agents in multi-agent system architecture. The models and data in the Cyber World provide an abstraction view of relevant entities in terms of the operational characteristics, dynamics, and architecture to achieve the production goals of RMS. Development of an appropriate Cyber World models for the entities in RMS is very important to effectively manage the Physical World of the CPS of RMS. The Cyber World of RMS in Figure 2 consists of the several types of agents, including task agent, resource agents, process agents and context generation agents. All these agents interact with each other by applying some negotiation protocol provided by the MAS platform. Operations of RMS are driven by a goal, usually specified by an order. A task agent aims to achieve the goal of production according to the order received. A process agent denotes the production process, usually specified by a process (workflow) model. A process (workflow) model is used to describe the operations to be performed on a task and the precedence constraints of these operations. A resource agent represents a resource such as a machine or a robot in RMS. A resource model is used to describe the types of activities (operations) supported by a resource agent. The role of a context generation agent is to generate context-aware information to guide resources and workers in RMS. Figure 1: Entities in Internet of Things (IoT)-enabled Manufacturing Environment. To generate context-aware information for resources/workers in RMS, a context generation agent must interact with relevant task agent, process agents, and resource agents. The internal architecture of a context generation agent include a model transformer module, an optimizer/analyzer module and a context generator. A context generation agent interacts with the relevant task agent, resource agents and process agents to obtain the goal, resource models, and process (workflow) models. The model transformer transforms the goal, resource models and process (workflow) models of agents into transformed models. Based on the transformed models obtained by the model transformer, optimization as well as analysis of solutions will be performed by the optimizer/analyzer to solve a configuration/scheduling feasibility problem. If the optimizer/analyzer can find a feasible solution, the solution will then be used as the input for the context generator. The context  
Figure 2: Providing Context-aware Information to Guide Resources/Workers. generator will create contextual information such as the operation lists for manufacturing resources to guide the operations of CPS. Although the architecture in Figure 2 provides a framework to design RMS based on the CPS paradigm, there still lack a study on how to systematically implement the model transformer, the optimizer/analyzer and the context generator for RMS. In this paper, we will focus on the development of models and a solution methodology needed to bridge the gap between theory and application in realizing the CPS paradigm for RMS. In the system architecture presented in Figure 2, there are several problems to be solved to implement a context-aware information system for RMS based on the CPS paradigm. These include collaborative interaction problem, model construction problem, model transformation problem, configuration/scheduling feasibility problem (CSFP) and context generation problem.