Reverse logistics network modelling
Reverse logistics is for all operations related to the reuse of products and materials. It is "the process of moving goods from their typical final destination for the purpose of capturing value, or proper disposal. Remanufacturing and refurbishing activities also may be included in the definition of reverse logistics." In order to model reverse logistics network from an economics point of view, the following simplified reverse logistics system has to be set. In this model the products are gathered from the consumers and transferred back to the producers, hence the direction of the flow in the  distribution supply chain is reversed and the model is expanded with the recovery center. First of all the used products are collected from the consumers and moved to the recovery center, where the condition of the products are examined according to their end of life cycle. If there is still recapture value, then the product is disassembled as preparation for further reprocessing, which means physical transformation to new customer. Otherwise the used product is disposed and transferred to the landfill site. According to the introduced model the main differences between forward and reverse logistics can be identified: Modeling techniques for  optimizing in reverse logistics network. In case of a reverse logistics network the nodes represent the different kind of facilities such as the manufacturers, distribution centers, recovery centers, ware houses. The opening of a facility is marked with a binary integer number. The links are acted for flow between facilities and the weights are continuous variables showing the quantity of flow. The two common way of designing reverse logistics network are the Mixed Integer Linear Programing (MILP) and Mixed Integer Non-Linear Programing (MINLP) methods, where the objective function, decision variables and constraint have to be defined Mixed Integer Linear  Programing (MILP). Remanufacturing model. This model is a two-level location problem with three type of facilities, integrated forward and reverse flow of goods. It means that the used items are gathered from consumers, transported back to plants and after remanufacturing get into the logistics network of new products. Objective function: Decision variables: Constraints: Refurbishment model. This model take into account just reverse flow of goods. Objective function: Decision variables: Constraints: Generic reverse logistics network model. Objective function: Decision variables: Constraints: This model can be further developed by introducing penalty cost for not collecting returned items and a compulsory minimal  disposal fraction as a feasibility technical constraints of reuse. Moreover, the static approach can be partly eliminated by multi-period programming, as a result trade-off between investment and operational cost and long run effect can be analyzed. Mixed Integer Non-Linear Programing (MINLP). The most severe drawback of MILP is the static aspect, hence MINLP try to relieve these restriction and develop further the existing model with dynamic elements, such as integrating cycle time, time and inventory positions. By this way uncertainty appears stronger in the model. The main objective is to maximize profit by determining the optimal number  of facilities in order to: Solution techniques of reverse logistics network models. Genetic algorithm. It is applicable for large size complex problems Main steps of the algorithm: Tabu search. The algorithm pursues local search and if it finds a local optimum it is prevented to get back formerly visited solution, which were recorded in the so-called tabu list