A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm

This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. All data are generated through computer simulation and saved in the file with csv format. There are 32 instances for these experiments. The datasets show the number of end-of-life products, the number of non-identical parallel processing lines and the number of processing cells in each processing line. The bifuzzy processing time and bifuzzy processing cost of each product remanufactured at each processing cell of each line and the bifuzzy processing reliability of each product remanufactured at each processing line also show in the datasets. Each bifuzzy variables in the data is composed of five groups of triangular fuzzy numbers with possibilities. The values in each group represent the smallest possible value of triangular fuzzy number, the most promising value of triangular fuzzy number, the largest possible value of triangular fuzzy number, and the possibility of this bifuzzy vairbale to take the triangular fuzzy number in this group by this bifuzzy variable.