Optimal Base-Stock Policy of the Assemble to Order Systems

In this work, an ordinal optimization based evolution algorithm (OOEA) is proposed to solve for a good enough target inventory level of the assemble to order (ATO) system. First, the ATO system is formulated as a combinatorial optimization problem with integer variables that possesses a huge solution space. Next, the genetic algorithm (GA) is used to select N excellent solutions from the solution space, where the fitness is evaluated with the radial basis function (RBF) network. Finally, we proceed with the OCBA technique to search for a good enough solution. The proposed OOEA is applied to an ATO system comprising 10 items on 6 products. The good enough target inventory level obtained by the OOEA is promising in the aspects of solution quality and computational efficiency.