A multi-phase scheduling method for reconfigurable flexible job-shops with multi-machine cooperation based on a Scout and Mutation-based Aquila Optimizer

With the increasing competition in the manufacturing market, reconfigurable manufacturing system (RMS) is gaining more and more attention because it can quickly respond to the changes in the market by adjusting manufacturing resources and organizational structure. Reconfigurable flexible job-shop (RFJS) is one of the main forms of RMS, which is composed of multiple reconfigurable manufacturing cells (RMCs). Each RMC contains several manufacturing equipment which can process a job simultaneously or continuously without multiple clamping. For RMS, both manufacturing resource configuration and production scheduling have direct impacts on its production efficiency, and they are strongly coupled and need to be considered simultaneously. In addition, multi-machine cooperative machining (MCM) has been realized and applied in various manufacturing scenarios with a wide application of intelligent manufacturing equipment such as mechanical arms. However, it is rarely considered in the production plan of RMS. Based on this background, this paper proposes a reconfiguration scheduling problem considering multi-machine cooperation (MCRSP) for RFJSs. Firstly, a multi-phase scheduling method and two manufacturing resource adjustment strategies are designed, and the mathematical model of MCRSP is established with reconfiguration costs and maximum completion time as evaluation indexes. Then, a three-layer coding method is designed, and a Scout and Mutation-based Aquila Optimizer (SMAO) is developed to solve the MCRSP model. In SMAO, the scout bee strategy and gaussian mutation strategy are introduced to improve the exploration ability in later iterations; a new leader selection method is proposed to avoid falling into local optimum prematurely; the balanced mechanism between exploitation and exploration is redesigned to ensure the search efficiency and convergence accuracy. A comparative experiment shows that SMAO contributes 18 optimal values among the 23 standard test functions, demonstrating the enhancement strategies' pertinence and effectiveness. Finally, the calculation and analysis of a practical case show that the proposed MCRSP can significantly improve production efficiency with a limited increase in reconfiguration costs, so it is suitable for practical engineering.