Linear Physical Programming Oriented Approach of Reverse Supply Chain Network Design for Costs and Recycling Rate

In the recent years, economic grows and population increasing bring consumptions for numerous amount of assembly products and materials resources all over the world. As the result of it, the material starvation has been getting a serious problem globally. In order to circulate materials from End-of-Life (EOL) assembly products, manufactures have to design reverse supply chain networks for the EOL products. The reverse supply chain includes transportation of the EOL products from collection centers to recovery facilities and/or a disposal facility. Then, the costs are required for recycling, transportation and facilities. Additionally, the EOL product statuses differ by users situation, and average recycling rate and cost of each product and part depend on the statues. To design the reverse supply chain network, decision maker (DM) decides transportation roots, the number of products flowed on each root and production volumes at each facility in order to minimize the total cost while maximizing the average recycling rate on the whole network. However, the relationship between the recycling rate and the costs become trade-off. Therefore, the DM has to solve them simultaneously. On the other hand, Linear Physical Programming (LPP) is known as a method to solve multi objective problems (Messac et al., 1996). It allows the DM to express his ideals as desirable ranges for each criterion. One of the most significant advantages using LPP is that the DM does not need to specify the weights for each criterion. This study designs a bi-objective reverse supply chain network to collect and recycle the EOL assembly products for the costs and the recycling rate using LPP. First, based on previous study (Ijuin et al., 2017), the reverse supply chain network is modeled to transport the EOL products from the collection centers to the recovery facilities depending on the EOL product status which includes the recycling cost and rate. Next, the reverse supply chain network is formulated with the LPP in order to minimize the total cost while maintaining the recycling rate of the whole network. Finally, a case study is conducted, the results by the LPP and the integer programming (Ijuin et al., 2017) are compared.