A decision support system for the design of a large electronics test facility

An optimization-based decision support system (DSS) for designing cost-efficient automatic test equipment (ATE) facilities is presented. The DSS combines efficient algorithms from cluster analysis, mixed-integer nonlinear programming, and closed queuing network theory in an iterative fashion to solve this problem. The DSS uses a hierarchical clustering algorithm to define test station configurations and to decompose a large, complex optimization problem into several moderately sized mixed-integer nonlinear programming problems. These problems are solved using a Lagrangian relaxation technique to determine the optimal distribution of the service workload among the test stations and to determine the number of test resources to be installed in each station; these solutions define the test facility design. The steady-state performance of these designs is evaluated using an approximate mean value analysis algorithm. The DSS allows sensitivity analysis with respect to changes in design objectives, test workload, and test facility configuration