Genetic algorithm for multi-item inventory control problem

The main purpose of this research is to apply an approximation approach – genetic algorithm to resolve the inventory control problems which maturely developed during 1960s to 1990s. However, it is still a tough work to deal with the multi-item inventory control optimization problems. Under the constraints on inventory space or budget limitations, to solve the multi-item inventory control problem by traditional approach, it is certainly in difficulty to collect the inventory data and in complexity to compute. Fortunately, an approach is applied into this study without the constraints on multi-item inventory system. It is so-called ‘optimal inventory policy surface’. This study utilizes the model of ‘optimal inventory policy surface’ and the genetic algorithms (GAs), because of easiness, to resolve the multi-item inventory control optimization problems. In this research, a systematically experimental design of Taguchi method is used to analyze the different settings of both parameters and different ranges of variables of ‘optimal inventory policy surface’ model using GAs as the calculation approach.