A scheduling procedure for a flow shop–like knowledgeable manufacturing cell with self-evolutionary features

As a component of knowledgeable manufacturing systems, the structure of flow shop–like knowledgeable manufacturing cells is similar to that of a flow shop, thus representing an NP-hard issue. Here, we propose a self-evolutionary algorithm that exhibits learning ability and is composed of learning and scheduling modules. Unlike traditional scheduling algorithms, whose performances remain unchanged when the procedure is coded, the performance of the algorithm proposed in this study gradually improves as the learning process continues. The self-evolutionary ability is realized through the training of a hybrid kernel support vector machine. The hybrid kernel support vector machine was designed to approximate the value of the Q-function to select the appropriate action for the scheduling module and thus to obtain the optimal solution. An iterative process of value based on the Q-learning was adopted to train the hybrid kernel support vector machine to gradually enhance the algorithm’s efficiency and accuracy. The extracted state features of the flow shop–like knowledgeable manufacturing cells serve as inputs to hybrid kernel support vector machine for easy generalization of the learning results. The action exerted on a feasible solution is also defined as the input of the hybrid kernel support vector machine. The computational results show that the performance of the proposed procedure improves as the learning process progresses. Data from the computation and comparisons with other algorithms verify the validity and efficiency of the proposed algorithm.

Self-Evolution of an Assembly Workshop in Knowledgeable Manufacturing Environment

In this study, the self-evolution problem of knowledgeable manufacturing systems is studied by taking an assembly workshop as an example. The rolling horizon procedure (RHP) is adopted to implement the self-evolution process of the workshop. The whole dynamic self-evolution process is decomposed into several static decision processes. At each decision point, a static decision sub-problem needs to be solved. A general mathematical model of these sub-problems is built, and a bi-level genetic algorithm (BiGA) is designed. Simulation results show that the model and algorithm are feasible and effective. By comparison, the system with self-evolution operations has a better production performance.