Based On The Dynamic Pricing Of Raw Materials Discarded Product Multi-objective Network Design

With the rapid economic development, constant depletion of resources, more and more serious damage to the environment, reverse logistics has been paid more and more attention. Making great effort to develop reverse logistics, and building a efficient recovery network to enhance the recycling of waste products, for achieving sustainable economic and social development, has very important significance.In real life, the waste products which provides raw materials, is mainly recycled by a third party. The number of recycling waste products is an important factor in product recovery network design, and the price strategy of recycling business is not only directly affect the number of recovery product, will also affect the recovery of business profits, so the price strategy of recovery business must be considered when building a product recovery network. However, currently stdudies of product recovery network design mainly assumed that the unit take-back cost is fixed. In view of this situation, this paper consider different game relations between the third party take-back business and manufacturing/r-emanufacturing business, build models for product recovery network design based on dynamic pricing for waste product recycling. Main work and results are as follows.(1) Based on more than one operating cycle, assuming there are processing capacity constraints in recycling center and inventories are allowed, two models were built for remanufacturing/remanufacturing business and the third party take-back business to choose their price strategies. In the process of modeling, two situations of centralized and decentralized decision-making were taken into account. Both the remanu-facturing/remanufacturing business and the third party take-back business consider their economic profit as the only objective. The example shows that the number of recovery product and the total profit of recovery system in the centralized decision-making situation are greater than in the decentralized decision-making situation.(2) On the basis of multi-period dynamic pricing of product recovery in the reverse logistics, multi-period and multi-objective product recovery network design was studied. Considering the profit of manufacturing/re-manufacturing business, the profit of the third party tack-back business and the economic interests of consumers, according to the different game situations between manufacturing/remanufacturing business and the third party tack-back business in the process of product recovery network design and dynamic pricing of product recovery, five models for product recovery network design in different decision-making situations were established. A example was conducted to compare the optimal product recovery network and the number of recovery product were compared among different models, the profits of each participant were also comparative analyzed.(3) On the basis of the models for product recovery network design in the determined environment, product recovery network design in the fuzzy environment was studied. We suppose that the supply of waste materials is the triangular fuzzy numbers, and analysis the feasibility and necessity of cooperation between the third party take-back business and manufacturing/remanufacturing business. In cooperation on the product recovery network design and pricing of product recovery, and considering two situations of centralized and decentralized decision-making when pricing of product recovery, two multi-period and multi-objective product recovery netwok models were built in the fuzzy environment. A numerical example is conducted to illustrate the efficiency of the model, fuzzy environment analysis and manufacturing third-party recyclers/re-manufacturers of the feasibility and necessity. In cooperation recovery network design and pricing under the premise of recycling, recovery pricing for centralized and decentralized pricing of two cases, were established under the fuzzy environment of multi-objective multi-cycle recovery network model.