摘要
为解决需求不确定的联合补充问题,提出一种基于改进模糊模拟的混合遗传算法。应用模糊集理论将需求处理为模糊变量,并用梯形模糊数表示,建立模糊期望值模型,改进经典模糊模拟,给出混合遗传算法,用于求解基本订购周期和最小期望值成本。与传统模糊模拟的混合遗传算法进行比较,结果表明,在相同条件下,该算法的期望成本偏差率更小。
In order to solve the Joint Replenishment Problem（JRP） with uncertainty demand, this paper proposes a hybrid Genetic Algorithm（GA） based on improved fuzzy simulation. According to fuzzy set theory, the demands are addressed as the fuzzy variables, using trapezoidal fuzzy number to formulate the expected value model. In order to reach the basic cycle time and the minimal expected value efficiently, a new fuzzy simulation based on the traditional fuzzy simulation and GA are integrated to produce a hybrid GA, and this algorithm is compared with the conclusion of the hybrid GA based on traditional fuzzy simulation. Comparison results show that the expectation cost deviation rate of this algorithm is smaller under the ~ame cnndition.
出处
《计算机工程》
CAS
CSCD
2013年第5期169-173,共5页
Computer Engineering
基金
黑龙江省自然科学基金资助项目(F200821)
哈尔滨市重点科技攻关计划基金资助项目(2011AA1CG063)
关键词
联合补充问题
期望值模型
模糊需求
梯形模糊数
遗传算法
模糊模拟
Joint Replenishment Problem（JRP）
expectation value model
fuzzy demand
trapezoidal fuzzy number
GeneticAlgorithm（GA）
fuzzy simulation
