A GA-Based Scheduling Algorithm for Semiconductor-Product Thermal Cycling Tests

In this paper, we study the scheduling problem of semiconductor-product thermal cycling tests (TCT), in which a batch of TCT test orders is issued to a testing machine. Each test order contains one or several test items, each with its own parameter conditions that must be satisfied. A machine has its capacity and overall equipment effectiveness (OEE). Additionally, order priorities are also considered. We propose a genetic-algorithm-based grouping scheduling method that handles grouping and scheduling at the same time with the objective of minimizing lateness. It groups genes and encodes chromosomes with variable lengths. Furthermore, to evaluate each chromosome, we propose a fitness function that simultaneously considers the delay time, number of test items, priority sequence, and equipment effectiveness rate. We also discuss strategies to eliminate non-feasible solutions after genetic operations. The effectiveness of the proposed method is validated through experiments using simulated production data and is compared against traditional methods. The experimental results for data with different densities of orders (off-peak season and peak season) show that the proposed method outperforms the others under the different types of orders.