Abstract: An improved multi-objective evolutionary algorithm is proposed for solving the flexible job-shop scheduling problem (FJSP) with released time and job-oriented multi-objective. The multi-objective FJSP optimization model is put forward, in which the makespan, the mean flow-time, total tardiness, total workload of machines, workload of the bottleneck machine and production cost widely concerned in complex manufacturing system are considered. According to the characteristics of the FJSP, an extended operation-based encoding and an active scheduling decoding mechanism are presented, an initial solution generation mechanism, and two effective crossover and mutation operations are designed for the genetic algorithm. In order to ensure convergence and the diversity of the solutions, an improved non-dominated sorting genetic algorithm (NSGA-II) is proposed. A set of Pareto solutions are obtained by the improved NSGA-II, and the analytic hierarchy process (AHP) approach is used to select the optimal compromise solution. The approach is tested on instances taken from the literature and practical data. The computation results validate the effectiveness of the proposed algorithm

Key words: Analytical hierarchy process, Flexible job-shop scheduling problem, Multi-objective evolutionary algorithm, Non-dominated sorting genetic algorithm II