Δυναμικά μοντέλα χωροθέτησης για το σχεδιασμό δικτύου αντίστροφης εφοδιαστικής αλυσίδας

Nowadays there are ever increasing demands on extended producer responsibility. Developed economies such as the US, Canada and the EU have established guidelines to oblige original equipment manufacturers (OEMs) to ensure the environmentally safe management of their products at the end of their life cycle. These guidelines, combined with the fact that considerable savings are achieved through the reuse or sale of products’ accessories and spare parts, have led many companies to face the challenge of strategic planning of a reverse logistics network, as this can lead to a competitive advantage and the creation of an environmental friendly strategy. The strategic planning of reverse logistics networks is a complex problem which involves determining the optimal locations and capacities of collection centers, inspection/disassembly centers, remanufacturing plants and recycling facilities. In this paper we present a mixed integer programming model along the lines of the framework proposed by Alumur et al. (2012). The model incorporates the structures required in a reverse logistics network in practice. The model was applied in the large household appliances sector of two separate case studies, concerning Spain and Greece. In both case studies the optimal locations of the reverse logistics network facilities were determined, at a 5 year planning horizon, in order to maximize the company's profits. Aiming at the same time at the achievement of greater profits within the existing model and at a new, even more realistic model, we investigated how the optimal design of reverse logistics network changed: I. according to different scenarios, which involved -demand changes for large household appliances, -selection of more cities as candidate locations for infrastructure installation, -changes of modules capacities that were added each period to inspection/disassembly centers and remanufacturing plants and -installation costs changes. II. according to structural changes to the original model, which involved -new parameters for separation of road and maritime transport costs, -changes in the existing constraints, so as more than one module could be added at each period to the existing facilities and -new constraints related to all kinds of costs, such as installation costs, costs for adding modules, transportation costs, operation costs, inventory costs and purchasing of new components. These new constraints represented the company's budget for the respective kind of cost. Overall, we present the results of both case studies and discuss how the topology of the network and the cost structure affect the optimal solutions.

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