摘要
针对煤炭企业内部供应链优化的高维、非线性问题,构建了以企业利润最大、客户满意度最高为目标函数,以各原煤矿井的原煤生产量、洗煤厂的洗选品种与洗选数量、客户对于企业的重要性、客户对于煤炭品种与规格以及数量和质量要求、煤炭到达目的地的运输方式等准则为约束条件的煤炭企业内部供应链优化模型.面向优化模型求解的难题,在传统粒子群优化算法(PSO)基础上,提出了一种改进的多目标粒子群优化算法(MOPSO),该算法在供应链优化方案生成时可以避免长时间的无效搜索,提高粒子群优化算法的求解效率.通过该方法对某煤炭企业内部供应链多目标优化模型进行仿真分析与计算,验证了该方案的正确性和有效性.
An internal supply chain optimization model for coal enterprises was established to solve the high dimension and nonlinear problems.The model takes profit and customer satisfaction maximum as the objective function and other criteria as constraints,which include the production of raw coal in each coal mine,washing categories and quantities of coal cleaning plant,the significance of customers to enterprises,the requirements of customers on coal types,specifications,quantity and quality,the mode of transportation when coals arrive on destination,etc.An improved multi-objective particle swarm optimization（MOPSO）algorithm was proposed based on the traditional particle swarm algorithm.By applying the new method,long-time invalid search during the generation of supply chain optimization scheme can be avoided,and the solving efficiency can be improved meanwhile.The simulation analysis of internal supply chain for a coal enterprise testified the effectiveness and the correctness of the improved MOPSO.
出处
《中国矿业大学学报》
EI
CAS
CSCD
北大核心
2016年第6期1300-1306,共7页
Journal of China University of Mining & Technology
基金
国家自然科学基金项目(71402179)
江苏省高校哲学社会科学研究项目(2016SJD630077)
江苏省教育科学"十二五"规划项目(B-b/2015/01/029)
