Solving a new bi-objective joint replenishment inventory model with modified RAND and genetic algorithms

There are many cases in real inventory systems where more than one objective must be optimized. The main purpose of this research is to develop a multiobjective joint replenishment problem (JRP), where one objective is the minimization of the total inventory investment and another is the minimization of the total inventory ordering and holding costs. To solve the suggested model, 3 algorithms are proposed. In the first algorithm, the existing RAND method, called the best heuristic for solving the JRP, is modified and a new heuristic algorithm is developed to be applicable to the JRP with 2 objectives. The second algorithm is a multiobjective genetic algorithm that has shown good performance for solving the JRP. Finally, a third algorithm is developed, using a combination of the 2 previous ones. The performances of these algorithms are then compared. Running the programs shows good performance in solving the 9200 randomly produced problems.