Modelling inventory grouping decisions using grouping genetic algorithms

Decision makers are often faced with the problem of grouping inventory into categories for cost-effective and efficient management and control of inventory. The classical ABC inventory analysis has been applied widely in industry. However, the approach is associated with practical limitations: the desired service level and budget allocation constraints are not considered simultaneously, there is no guarantee for optimal solutions, and qualitative decision criteria are not modelled explicitly. It is desirable to develop models that can capture quantitative and qualitative criteria, from a multi-criteria optimization view point. In light of these limitations, the purpose of this research is to model the inventory grouping problem using grouping genetic algorithms approach. We first assess the grouping structure of the inventory classification problem, and then model the grouping problem from the grouping genetic algorithm perspective. Further research prospects and applications are evaluated and presented.