摘要
对用PSO算法解决需求为不确定的联合补充问题进行了研究。运用模糊规划方法处理需求为模糊变量的联合补充问题,得到了作为求解目标的模糊数学模型;采用PSO思想对该模型进行分析,转化为PSO问题模型,制定出算法流程,并用数值实例验证了提出的粒子群优化模型和求解算法的有效性;对随机生成的大量数据进行处理,结果证明问题规模相同时该算法较遗传算法具有更高的效率。
The solution to the Joint Replenishment Problem（JRP） with fuzzy resource constraint by PSO algorithm is studied. Fuzzy programming method is used to deal with the joint replenishment problem with fuzzy demands, and the fuzzy mathematical model is got. The model is analyzed by PSO method, and converted into a PSO problem model. The algorithm process is worked out. A numerical example shows the effectiveness of the proposed particle swarm optimization model and the algorithm. Much data randomly generated is processed. The results prove that to solve problems under the same scale, this PSO algorithm has a higher efficiency than genetic algorithm.
出处
《计算机工程与应用》
CSCD
2012年第35期238-242,共5页
Computer Engineering and Applications
基金
黑龙江省自然科学基金(No.F200821)
关键词
粒子群优化(PSO)算法
模糊需求
联合补充问题
Particle Swarm Optimization（PSO） algorithm
fuzzy requirement
joint replenishment problem
