粒子群算法在Lot-sizing问题中的应用

针对无能力限制的Lot-sizing问题,提出一种改进的离散粒子群优化算法.设计粒子编码为生产设备的调整状态,通过有效的解码程序将粒子解释为生产计划.区别于传统的粒子群算法,算法采用单切点交叉算子来提高算法的局部求精能力,并引入变异算子和速度扰动策略保持种群的多样性,使算法在局部求精和空间探索间取得了较好的平衡.在随机生成的90组测试实例中对算法性能进行仿真实验,结果表明该算法具有良好的性能.

Application of particle swarm optimization algorithm in Lot-sizing problem

An improved discrete particle swarm optimization(PSO) algorithm is designed to tackle the general uncapacitated Lot-sizing problem.The encoding scheme of particles is designed in terms of setup states of production units,while an effective decoding procedure translates a particle into a feasible production plan.Different from the traditional PSO algorithm,the improved PSO algorithm incorporates single cutting-point crossover operators to improve the intensification ability of the algorithm.In addition,mutation operators and velocity disturbance strategies are also introduced into the PSO algorithm to keep the diversity of swarm.By using those operators,the proposed algorithm can get good balance between exploitation and exploration.Computational results on 90 randomly generated test instances show the good performance of the proposed PSO algorithm.