ddeveloping and solving a bi-objective joint replenishment problem under storing space constraint

In this research, a bi-objective joint replenishment problem has been developed and solved with the assumption of one restricted resource. The proposed model has a storing space constraint and tries to optimize two objective functions simultaneously. They include minimizing annual holding and setup costs and minimizing annual inventory investment. Then, for solving this problem, a multi-objective genetic algorithm (MOGA) has been developed. In order to analyze the algorithm efficiency, its performance has been examined in solving 1600 randomly produced problems using parameters extracted from literature. The findings imply that the proposed algorithm is capable of producing a good set of Pareto optimal solutions. Finally, the application of the problem solving approach and the findings of the proposed algorithm have been illustrated for a special problem, which has been randomly produced.