A greedy-based crow search algorithm for semiconductor final testing scheduling problem

Abstract

The semiconductor final testing scheduling problem (SFTSP) is of great importance to the efficiency of integrated circuit firms and has been widely investigated in the field of intelligent optimization. In this paper, a greedy-based crow search algorithm (GCSA) is presented for solving the SFTSP. According to the characteristics of SFTSP, new encoding and decoding strategies are proposed to link the feasible solutions to the scheduling schemes. The search operations are performed only in the operation sequence space, and a corresponding machine allocation vector is generated for each operation sequence vector based on the greed mechanism. Two crow position update strategies named track and hover are redesigned and the improved crow search algorithm is utilized to search the operation sequence space efficiently in order that the GCSA can adapt the SFTSP and make full use of the information obtained during the search process. Moreover, the effect of parameters is investigated based on a multi-factor analysis of variance (ANOVA) approach. Finally, extensive computations and comparisons on ten test instances derived from the practical production demonstrate that the proposed GCSA outperforms the state-of-the-art methods in the literature to solve the SFTSP.