Solving the Multi-Buyer Joint Replenishment Problem with a Modified Genetic Algorithm

The Joint Replenishment Problem (JRP) is a multi-item inventory problem. The objective is to develop inventory policies that minimizes the total costs (comprised of holding cost and setup cost) over the planning horizon. In this paper, we look at the multi-buyer, multi-item version of the JRP. We propose a new modified genetic algorithm which is partially parallelizable and very efficient. Tests are conducted on problems from a leading bank in Hong Kong and from the literature.