We develop a multi-objective model in a multi-product inventory system. The proposed model is a joint replenishment problem (JRP) that has two objective functions. The first one is minimization of total ordering and inventory holding costs, which is the same objective function as the classic JRP. To increase the applicability of the proposed model, we suppose that transportation cost is independent of time, is not a part of holding cost, and is calculated based on the maximum of stored inventory, as is the case in many real inventory problems. Thus, the second objective function is minimization of total transportation cost. To solve this problem three efficient algorithms are proposed. First, the RAND algorithm, called the best heuristic algorithm for solving the JRP, is modified to be applicable for the proposed problem. A multi-objective genetic algorithm (MOGA) is developed as the second algorithm to solve the problem. Finally, the model is solved by a new algorithm that is a combination of the RAND algorithm and MOGA. The performances of these algorithms are then compared with those of the previous approaches and with each other, and the findings imply their ability in finding Pareto optimal solutions to 3200 randomly produced problems.
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