A knowledge-based multi-agent evolutionary algorithm for semiconductor final testing scheduling problem

The final testing process ensures the quality of the products in the semiconductor manufacturing factory. Scheduling for the final testing process is crucial to the economic efficiency of production. In this paper, an effective knowledge-based multi-agent evolutionary algorithm (KMEA) is proposed for solving the semiconductor final testing scheduling problem (SFTSP). In the KMEA, each agent is represented by a solution, which is a combination of the operation sequence vector and the machine assignment vector. A hybrid initialization mechanism is proposed to balance the diversity and the quality of the initial agents. In each iteration of evolution, the agents evolve by mutual-learning and competition based on the model of agent lattice. Moreover, a knowledge base is employed to store the useful information during the search process. The knowledge base is used to generate new agents in the competition phase. The computational complexity of the KMEA is analyzed, and the influence of parameter setting is also investigated. Finally, numerical simulation and comparisons are provided to demonstrate the effectiveness and efficiency of the KMEA in solving the test instances.