A differential evolution algorithm for joint replenishment problem using direct grouping and its application

There has been much work in establishing joint replenishment model and designing effective and robust algorithms. Little research has been done by direct grouping methods. In this paper, we present a differential evolution (DE) algorithm that uses direct grouping to solve joint replenishment problem (JRP). Extensive computational experiments are performed to compare the performances of the DE algorithm with results of evolutionary algorithm (GA). The experimental results indicate that the DE algorithm can find a replenishment policy that incurs a lower total cost than the GA. We also conducted a case study to test the proposed DE algorithm for the JRP. The findings suggest that the proposed model is successful in decreasing spare parts ordering costs and holding costs significantly in a power plant.