一种基于计算机应用的多周期随机优化问题的求解方法
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摘要
在失效风险与需求波动并存的不确定性环境下,为了解决商业利润优化问题,获得最低风险最大利润,文章提出了一种基于多周期随机规划问题的求解场景的新方法,并对某国的一个大型市场的商业政策进行了案例研究。在基于分解的定价方法基础上,对新方法进一步说明。利用基于分解的定价方法求解多周期随机优化问题是随机规划领域的最新工作,基于分解的定价方法是一种最新的、更快的分解技术。针对优化技术的特点,文章提出了一个计算模型,从该国的大型市场收集了一年的数据,并分析了其利润,根据不确定因素下的不同场景将全年分为4个阶段。最后通过使用数学编程语言对新技术的模型进行了分析。利用提出的模型,经过对比分析,采用提出的计算模型,在分析利润方面有明显的优势,而且也能够在实际商业政策中得到应用。
In the uncertain environment with the coexistence of failure risk and demand fluctuation,in order to solve the problem of commercial profit optimization and obtain the maximum profit of the lowest risk,this paper presents a new method for solving scenarios based on multi-cycle stochastic programming,and makes a case study on the commercial policy of a big market in a country.Based on the decomposed pricing method,this paper studies a new method,which is the latest and faster decomposition technology.It is the most recent work in the field of stochastic programming to solve the multi-cycle stochastic optimization problem by using the decomposition based pricing method.Aims to the characteristics of optimization technology,a calculation model is put forward.From a national super store market data collection for a year,it analyzes their profits,and according to different scenarios under the uncertainty of the year,it is divided into four stages.Finally,the model of new technology is analyzed by using mathematical programming language.By using the proposed model,it has obviously profit advantage and can be applied in actual business policy.
In the uncertain environment with the coexistence of failure risk and demand fluctuation,in order to solve the problem of commercial profit optimization and obtain the maximum profit of the lowest risk,this paper presents a new method for solving scenarios based on multi-cycle stochastic programming,and makes a case study on the commercial policy of a big market in a country.Based on the decomposed pricing method,this paper studies a new method,which is the latest and faster decomposition technology.It is the most recent work in the field of stochastic programming to solve the multi-cycle stochastic optimization problem by using the decomposition based pricing method.Aims to the characteristics of optimization technology,a calculation model is put forward.From a national super store market data collection for a year,it analyzes their profits,and according to different scenarios under the uncertainty of the year,it is divided into four stages.Finally,the model of new technology is analyzed by using mathematical programming language.By using the proposed model,it has obviously profit advantage and can be applied in actual business policy.
引文
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[2]尹力博,韩立岩.基于多阶段随机规划模型的国债动态积极投资策略[J].中国管理科学,2015,23(6):9-16.
[3]何晓旭,殷守林,赵志刚.一种新的无优化约束问题的混合FR和PRP共轭梯度算法[J].沈阳师范大学学报(自然科学版),2016,34(1):92-95.
[4]SAKAWA M,YANO H,NISHIZAKI I.Stochastic Linear Programming[M]∥Linear and Multiobjective Programming with Fuzzy Stochastic Extensions.Springer US,2013:149-196.
[5]TEMPELMEIER H,HILGER T.Linear programming models for a stochastic dynamic capacitated lot sizing problem[J].Computers&Operations Research,2015,59(C):119-125.
[6]STOCKBRIDGE R,BAYRAKSAN G.Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming[J].Computational Optimization&Applications,2016,64(2):407-431.
[7]TOPALOGLU H,POWELL W B.Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems[M].INFORMS,2006.
[8]WALLACE S W,ZIEMBA W T.Applications of Stochastic Programming(Mps-Siam Series on Optimization)(MpsSaimseries on Optimization)[M].Society for Industrial and Applied Mathematics,2005.
[9]HAAG E V D.The Year 2000:A Framework for Speculation on the Next Thirty-three Years.by Herman Kahn;Anthony J.Wiener[J].Political Science Quarterly,1967,83(4):663.
[10]翁朝敏.目标分解与执行[J].标签技术,2015(6):64-68.
[11]MCNAMARA P,MCLOONE S.Hierarchical Demand Response for Peak Minimization Using Dantzig-Wolfe Decomposition[J].IEEE Transactions on Smart Grid,2015,6(6):2807-2815.
[12]苏尔.矩阵前主子式的三角分解改进[J].计算机科学,2017,44(s2):148-153.
[13]韩丹丹.拉格朗日乘数法解决一类极值问题[J].数学通讯,2017(18):60-63.
[14]王乐洋,赵英文,陈晓勇,等.多元总体最小二乘问题的牛顿解法[J].测绘学报,2016,45(4):411-417.
[15]吴晓.求解工程中静不定结构内力的通用方法[J].中南大学学报(自然科学版),2016,47(1):262-272.