Today, most industrial managers in the world are interested in protecting the environment and biological resources. On the other hand, current technologies are getting momentum towards specialization and globalization. Thus, in order to remain in a highly competitive world market, producers have to respond to the customers' demands under different circumstances. The leading role of distribution centers to deliver products to customers on time and to reduce the costs of stock maintenance has attracted the attention of many supply chain managers in current competitive conditions. Cross docking is a logistic strategy aiming to reduce the stock and increase the level of customer's satisfaction. Products are delivered from the supplier to the customers through cross docking. In this paper, a nonlinear multiproduct vehicle location-routing model is presented with heterogeneous vehicles. Each truck can carry one or more types of products. In other words, compatibility between product and vehicle has been accounted for here. This model aims to find out the possible minimum number of cross dockings among the existing set of discrete locations and minimize the total cost of opening cross docking centers as well as vehicle transportation (distribution and operation cost) costs. In sum, the model aims to find the number of cross docking centers, the number of vehicles and the best route in the distribution network. Since the model is mixed integer programming, to apply the model to medium and large scale problems, meta innovative genetic and particle swarm optimization algorithms are introduced. The results obtained from examining various problems show high efficiency of the proposed methods.

Cross docking play an important role in management of supply chains where items delivered to a warehouse by inbound trucks are directly sorted out, reorganized based on customer demands, routed and loaded into outbound trucks for delivery to customers without virtually keeping them at the warehouse. If any item is held in storage, it is usually for a short time, which is normally less than 24 hours. The proposed model of this paper considers a special case of cross docking where there is temporary storage and uses GRASP technique to solve the resulted problem for some realistic test problems. In our method, we first use some heuristics as initial solutions and then improve the final solution using GRASP method. The preliminary test results indicate that the GRASP method performs better than alternative solution strategies.

Cross docking is one of the most important issues in management of supply chains. In cross docking, different items delivered to a warehouse by inbound trucks are directly arranged and reorganized based on customer demands, routed and loaded into outbound trucks for delivery purposes to customers without virtually keeping them at the warehouse. If any item is kept in storage, it is normally for a short amount of time, say less than 24 hours. In this paper, we consider a special case of cross docking where there is temporary storage and implements genetic algorithm to solve the resulted problem for some realistic test problems. In our method, we first use some heuristics as initial solutions and then improve the final solution using genetic algorithm. The performance of the proposed model is compared with alternative solution strategy, the GRASP method.