The constrained joint replenishment problem using direct and indirect grouping strategies with genetic algorithms

Abstract

The Joint replenishment problem (JRP) is a model for inventory optimization when ordering multiple products is required. This model allows reducing total inventory costs compared to the practice of performing individual optimization of each product. The saving cost is produced by sharing the fixed ordering costs between several products. The JRP model can be solved using two strategies: the direct grouping strategy (DGS) and the indirect grouping strategy (IGS), which vary in the way the products are grouped. As it can be seen in this chapter, several authors use the JRP model in its simplest version, but it can be easily extended to include restrictions for approximate the model to more realistic applications. Due to the combinatorial nature and the restriction inclusion to the JRP model, it is necessary to use advanced techniques that allow obtaining good solutions in reasonable computing times. This chapter presents the JRP with resource and capacity constraints, which is solved using two genetic algorithms for the evaluation of the Indirect and the Direct Grouping Strategies (IGS and DGS).