In recent years, the semiconductor final testing scheduling Problem (SFTSP), recognized as a unique multiresource scheduling challenge, attracts increasingly attention of academia and industry in the semiconductor man...
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In recent years, the semiconductor final testing scheduling Problem (SFTSP), recognized as a unique multiresource scheduling challenge, attracts increasingly attention of academia and industry in the semiconductor manufacturing process. In this paper, a novel estimation of distribution algorithm combined with Q-learning (QEDA) is proposed to solve the SFTSP. According to the characteristics of the used operation encoding, a new probability matrix update mechanism is proposed for enhancing the priority relationships among operations. Considering that the traditional EDA is not in favor of local exploitation compared with its global exploration, a reinforcement learning is designed to improve the performance of the proposed algorithm. Furthermore, for the challenge of the resource allocation in SFTSP, four actions are introduced based on the variance of individual objectives. Extensive numerical simulations and comparative experiments show that the proposed QEDA algorithm exhibits much better performance than the state-of-the-art algorithms in the literature for solving the SFTSP.
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,...
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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 ma-chine 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 compari-sons on ten test instances derived from the practical production demonstrate that the proposed GCSA out-performs the state-of-the-art methods in the literature to solve the SFTSP.
In this paper, we address a semiconductorfinaltesting problem from the semiconductor manufacturing process. We aim to determine both the assignment of machines and the sequence of operations on all the machines so a...
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In this paper, we address a semiconductorfinaltesting problem from the semiconductor manufacturing process. We aim to determine both the assignment of machines and the sequence of operations on all the machines so as to minimize makespan. We present a cooperative co-evolutionary invasive weed optimization (CCIWO) algorithm which iterates with two coupled colonies, one of which addresses the machine assignment problem and the other deals with the operation sequence problem. To well balance the search capability of the two colonies, we adopt independent size setting for each colony. We design the reproduction and spatial dispersal methods for both the colonies by taking advantage of the information collected during the search process and problem-specific knowledge. Extensive experiments and comparison show that the proposed CCIWO algorithm performs much better than the state-of-the-art algorithms in the literature for solving the semiconductor final testing scheduling problem with makespan criteria.
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