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. The first folder "1. Additional data used to evaluate the proposed model" contains the addition data required to evaluate the proposed model, the second folder "2. Additional data used to verify the performance of proposed algorithm" includes 38 instances used to verify the performance of proposed algorithm. 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. In particular, the first folder also contains the addition data applicable to deterministic model (indicated by the notes in the name of the file, i.e., "for deterministic model"), which directly gives the processing time and processing cost of the end-of-life products on the corresponding processing line.