A discrete particle swarm optimisation for operation sequencing in CAPP
提出一种可行序列导向的离散粒子群优化算法，通过交叉更新和自适应变异高效搜索满足约束的工序序列，以最低加工成本为目标，在案例中优于多种现有算法。
Operation sequencing is one of crucial tasks for process planning in a CAPP system. In this study, a novel discrete particle swarm optimisation (DPSO) named feasible sequence oriented DPSO (FSDPSO) is proposed to solve the operation sequencing problems in CAPP. To identify the process plan with lowest machining cost efficiently, the FSDPSO only searches the feasible operation sequences (FOSs) satisfying precedence constraints. In the FSDPSO, a particle represents a FOS as a permutation directly and the crossover-based updating mechanism is developed to evolve the particles in discrete feasible solution space. Furthermore, the fragment mutation for altering FOS and the uniform and greedy mutations for changing machine, cutting tool and tool access direction for each operation, along with the adaptive mutation probability, are adopted to improve exploration ability. Case studies are used to verify the performance of the FSDPSO. For case studies, the Taguchi method is used to determine the key parameters of the FSDPSO. A comparison has been made between the result of the proposed FSDPSO and those of three existing PSOs, an existing genetic algorithm and two ant colony algorithms. The comparative results show higher performance of the FSDPSO with respect to solution quality for operation sequencing.