Abstract
With the development of industrial manufacture in the context of Industry 4.0, various advanced technologies have been designed, such as reconfigurable machine tools (RMT). However, the potential of the latter still needs to be developed. In this paper, the integration of RMTs was investigated in the capacity adjustment of job shop manufacturing systems, which offer high flexibility to produce a variety of products with small lot sizes. In order to assist manufacturers in dealing with demand fluctuations and ensure the work-in-process (WIP) of each workstation is on a predefined level, an operator-based robust right coprime factorization (RRCF) approach is proposed to improve the capacity adjustment process. Moreover, numerical simulation results of a four-workstation three-product job shop system are presented, where the classical proportional–integral–derivative (PID) control method is considered as a benchmark to evaluate the effectiveness of RRCF in the simulation. The simulation results present the practical stability and robustness of these two control systems for various reconfiguration and transportation delays and disturbances. This indicates that the proposed capacity control approach by integrating RMTs with RRCF is effective in dealing with bottlenecks and volatile customer demands.
Highlights
As customer demand is changing quickly, production and logistic systems become more complex and dynamic
This paper focuses on developing an effective capacity control strategy for the job shop system by means of reconfigurable machine tools (RMT)
An operator-based approach incorporating RMTs was developed to control the capacity of job shop systems
Summary
As customer demand is changing quickly, production and logistic systems become more complex and dynamic. Various delays, stochastic disturbances, and bottlenecks in the production system induce additional challenges for manufacturers To deal with these problems, numerous advanced technologies, such as reconfigurable machine tools (RMT), Internet of Things (IoT), radio-frequency identification (RFID), and cyber-physical systems (CPS), have been proposed for a more automatic, accurate, and reliable monitoring and controllable manufacturing system [1,2,3]. Other algorithms, such as tabu search [8], genetic algorithm (GA) [9], genetic programming based hyper-heuristic approach (GA-HH) [10], and particle swarm optimization (PSO) [11], were applied to the flexible job shop scheduling problem The aim of these works was to improve productivity as well as to minimize cost and completion time.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
