Abstract In this study, we developed a Joint Replenishment Problem (JRP) model; In addition, JRP was introduced through capacitated vehicle to send the parts. The model was developed as an Integer-non linear model. Cost function consists of the major ordering cost, the minor ordering cost and the holding cost. Previous studies had not considered transaction cost; therefore, we considered transaction cost, which depends on the number of different parts in the order. Demand for parts was considered as constant and the model was solved by using a genetic algorithm. For calculating variable, we developed the present algorithm in the literature. We solved the model for two conditions, with dispatch volume restriction and without dispatch volume restriction. The results showed that the dispatch volume increases both the cycle time and the total cost. The model was solved for one of the major manufacturers of SAPCO. We considered the problem with 12 strategic parts of this company. The model was investigated through the real data gathered from the SAPCO Company. Finally, for model validation, the results were compared with the traditional ordering system of SAPCO, in which the parts are ordered separately. It was shown that the proposed system has better performance than the traditional ordering system. Validation of Model was performed by the numerical example that has been solved in the literature. Keywords: Joint replenishment, Automotive parts, Genetic algorithm, Inventory.

Abstract Today the reverse logistics is one of the most important subjects in supply chain area. The control of return process beside the forward supply chain permits the organization to avoid of additional costs. This paper at the first approach introduces the major success factors which result from interview with experts in this area and although stemmed from major resources in automotive reverse logistics and then it utilizes the Interpretive Structural Modeling (ISM) methodology to understand the mutual influences among the barriers so that those driving success factors, which can aggravate few more factors and those independent factors, which are influenced by driving factors are identified. By analyzing the success factors using this model, we may extract crucial factors that influence the reverse logistics activities. It can be observed that there are some factors, which have both high driving power and dependency, thus they need more attention. Finally, the implications for practice and future research are discussed.