# Reconfiguration Decision-Making of IoT based Reconfigurable Manufacturing Systems
## 2 Related Works  
A RMS can provide flexible manufacturing through reconfiguration. However, for a RMS to reach its potential, it is necessary to address various additional issues such as reconfiguration decision making and maintenance. Various studies have been conducted on these issues. [PERSON] et al. [1] described the definitions of RMS and technical situations by reviewing various studies on RMS. The research is meaningful to the point that it includes diverse definitions of RMS and provides an integrated view of RMS. [PERSON] et al. [4] reviewed various studies on RMS and described a current research direction and the applicability of the latest technologies from many different perspectives. This study suggested various applications of emerging technologies (e.g., IoT, cloud computing, and artificial intelligence) to RMS. [PERSON] et al. [5] suggested reconfigurable strategies for RMS in various levels of the manufacturing system. The suggested strategies were applied to inter-cell levels, machining cell levels, and process monitoring system levels. [PERSON] et al. [6] studied an algorithm to generate a plan for the reconfiguration and maintenance of RMS. They proposed an algorithm for establishing a plan in consideration of each machine's reliability and failure, and the costs for replacement and failure by applying a reconfigurable maintenance time window (RMTW). [PERSON] et al. [7] proposed a genetic algorithm-based algorithm for reconfiguration decision-making in RMS, and suggested a reconfiguration decision-making method according to a certain productivity level. The method is for minimizing changes and maximizing productivity under restrictions of different available processes according to machines and of different required processes according to goods. [PERSON] et al. [8] focused on the reconfiguration of the assembly system of RMS and discussed the production planning algorithm for the assembly scheduled at a minimized reconfiguration cost and with maximized reconfiguration gains. They proposed an algorithm that had various objective functions considering cost and delay time for reconfiguration, work balance, due date, etc. [PERSON] et al. [9] suggested a reconfiguration-points decision algorithm based on complexity. Information-based entropy theory was applied to calculate complexity. Based on complexity, the proposed algorithm calculated a machine state probability to make reconfiguration decisions. [PERSON] and [PERSON] [10] suggested a negotiation-based planning algorithm for RMS. They assumed a manufacturing system with self-aware machines and applied the Petri-net to model the behavior of the system and suggested a negotiation policy for machine selection. [PERSON] [11] suggested a meta-heuristic approach for dynamic process planning in RMS. He modeled the behavior of their self-aware manufacturing system with the Petri-net method, which applied a discrete particle swarm algorithm to solve the planning problem. The suggested algorithm performed well in a simulation experiment. [PERSON] et al. [12] suggested a reconfiguration method for the holonic manufacturing system and proposed an agent-based approach, in which each machine negotiates for job allocation and connection with another machine. Their method is based on the assumption that entries of every demand trigger reconfiguration. [PERSON] et al. [13] researched the pallet-fixture allocation of reconfigurable modular cells and studied an algorithm for pallet and fixture allocation in an RMS. The proposedalgorithm was verified in simulation experiments and the results showed that the algorithm was applicable to the reconfiguration decision-making of pallets and fixtures. With improvements in sensor technologies and telecommunication technologies, it is possible to acquire information rapidly from IoT sensors. To improve the performance of RMS, many studies have been conducted on the application of IoT sensors to RMSs.