摘要
研究了随机性需求环境下的供应商优选与订货量分配问题,构建了以质量、成本、交货期3个准则为目标函数、以其他目标为约束条件的多目标随机约束规划模型;借助于加权法和罚函数法,将多目标随机约束规划模型转化为单目标优化模型;进而设计了带惯性因子和收缩因子的粒子群优化求解算法对所建模型进行求解,并与常用的遗传算法求解方法进行对比分析,实例验证了粒子群优化算法解决此类优化问题的可行性和有效性。
The vendor selection and the order quantity allocation problems with stochastic demand are studied. A multi-objective and stochastic constraint planning model is constructed with the objective functions under three criteria of quality, cost and delivery, and the other goals as constraint conditions with the stochastic demand. By using the weighted way and the penalty function, the stochastic model with uncertainty and multi-objective is converted into a single target optimization model. Then, an improved particles swarm optimization algorithm （PSOA） with inertia and contraction factors is designed to solve the proposed model, and comparative analysis with commonly-used genetic algorithm is given to verify the feasibility and efficiency of the PSOA applied to such issues.
出处
《中国管理科学》
CSSCI
北大核心
2009年第6期98-103,共6页
Chinese Journal of Management Science
基金
国家自然科学基金资助项目(70801030)
关键词
多产品采购
供应商选择
订货量分配
随机需求
粒子群优化算法
multi-production purchase
vendor selection
order quantity allocation
stochastic demand
particles swarm optimization algorithm
