A genetic optimization algorithm for nonlinear stochastic programs in an automated manufacturing system

Abstract

We concern with both time and cost optimization in an automated manufacturing system. In our proposed system, material handling is carried out by automated guided vehicles and robots for the manufacturing functions. In this way, defect rates, breakdown times, waiting time, processing time, and certain other parameters are not expected to be deterministic. Therefore, we apply stochastic programming to optimize the production time and material handling cost. The proposed stochastic program is a nonlinear model, for this reason we apply a successive linear programming (SLP) technique for its optimization. Numerical test results point to the inefficacy of the proposed optimization method for large sized problems. Hence, a genetic algorithm (GA) is presented to optimize large sized problems.