CAJ | 학술논문

针对流水车间批调度问题，提出一种基于群智能算法的求解思路。结合问题具体特点，给出工件集合的分批策略，设计了将Palmer和Best Fit（BF）分批规则相结合的分批方法；在批排序阶段，提出了一种改进的微粒群算法；在粒子初始生成阶段，通过引入NEH启发式算法改进了粒子的初始化质量；在全局最佳位置更新前，通过变邻域搜索优化了算法的局部搜索能力，避免了算法陷入局部最优。仿真实验表明，改进后的算法优于传统的微粒群算法和NEH启发式算法。
침대류수차간비조도문제，제출일충기우군지능산법적구해사로。결합문제구체특점，급출공건집합적분비책략，설계료장Palmer화Best Fit（BF）분비규칙상결합적분비방법；재비배서계단，제출료일충개진적미립군산법；재입자초시생성계단，통과인입NEH계발식산법개진료입자적초시화질량；재전국최가위치경신전，통과변린역수색우화료산법적국부수색능력，피면료산법함입국부최우。방진실험표명，개진후적산법우우전통적미립군산법화NEH계발식산법。
An approach based on swarm intelligence is presented to solve the problem of scheduling tasks on flow-shop with batch processing machines. According to the characteristics of the problem under study, a method based on Palmer and Best Fit heuristic algorithm is developed to form batches. Moreover, an improved Particle Swarm Optimization(PSO)algorithm is pre-sented to sequence the obtained batches. In PSO, the NEH heuristic is employed to improve the quality of the initial population. In order to enhance the search capabilities of the proposed algorithm, a variable neighborhood searching is performed for each iteration before the global best position is updated. The experimental results show that the proposed algorithm has a better effec-tiveness than the standard PSO algorithm and the NEH heuristic.