LAUSR

This study examined the test scheduling optimization problem of industrial robot servo system (IRSS) samples with unequal numbers of different types of test items on the multiple IRSS comprehensive test platforms with the same function. In order to improve IRSS performance test efficiency, a multi-station test scheduling optimization method, which combines IRSS sample-level scheduling and test item-level scheduling, was proposed. First, the IRSS sample-level scheduling was carried out, and a model was established with reference to the identical parallel machine problem (IPMP). The optimal result of IRSS sample-level multi-station scheduling was obtained by solving the model. Second, based on the result of IRSS sample-level multi-station scheduling, and taking the ideal optimal test completion time of IRSS multi-station scheduling as the target, the test items at the stations were reallocated to obtain the optimal scheduling result. Finally, an application example was used to verify the efficiency of the proposed method, and the experiment result showed that the proposed method can effectively fulfill the optimal allocation of different IRSS test items of multiple IRSS samples on multiple IRSS comprehensive test platforms. The test time was 247 min shorter than the conventional sequential parallel test and only one min longer than the ideal optimal test completion time.