报童模型两阶段表示

本文介绍了报童模型的基本概念及应用两阶段随机规划方法进行求解的过程。通过分解确定性和随机性的变量,文章详细阐述了两种不同的两阶段建模思路。

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这几天读了随机规划入门《Introduction to Stochastic Programmingh》这本书,发现报童模型可以用两阶段法来表示。

报童模型描述:一个报童每天采购一定数量的报纸,报纸需求是随机的,卖不完的报纸可以折价退回,各参数如下
单份报纸售价: ppp
单份报纸采购价:ccc
单份报纸退回价:vvv
报纸需求量: ξ\xiξ,概率密度函数 f(ξ)f(\xi)f(ξ),累计分布函数 F(ξ)F(\xi)F(ξ)

决策变量:报纸采购量 xxx

一般建模:
min⁡cx−pmin⁡{x,ξ}−v(x−ξ)+\min\quad cx-p\min\{x,\xi\}-v(x-\xi)^+mincxpmin{x,ξ}v(xξ)+

最优解:
x∗=F−(p−cp−v)x^\ast=F^-(\frac{p-c}{p-v})x=F(pvpc)

1. 第一种思路

书中的两阶段建模,其内涵是将模型中的确定变量与随机变量分开,各为一阶段:
第一阶段:min⁡L(x)=pmin⁡{x,ξ}+v(x−ξ)+\min\quad L(x)=p\min\{x,\xi\}+v(x-\xi)^+minL(x)=pmin{x,ξ}+v(xξ)+
第二阶段:min⁡cx−L(x)\min\quad cx-L(x)mincxL(x)

2. 第二种思路

还有一种两阶段建模的思路:第一阶段的决策变量为订货量 xxx(在观测到需求之前),第二阶段的决策变量为销售量 yyy (在观测到需求之后, y≤D,y≤xy\leq D, y\leq xyD,yx, D 为观测到的需求),具体为:

两阶段决策的总体模型为:

min⁡cx+E[Q(x,ξ)] \min\quad cx+\mathbb{E}[Q(x, \xi)] mincx+E[Q(x,ξ)]

其中, Q(x,ξ)Q(x, \xi)Q(x,ξ) 为第二阶段的目标函数,具体为:

min⁡yQ(x,ξ(w))=−py−v(x−D)+s.t.−y≥−x−y≥−Dy≥0 \begin{aligned} &\min_y\quad &&Q(x, \xi(w))=-py-v(x-D)^+\\ &s.t.\\ &&&-y\geq -x\\ &&&-y\geq -D\\ &&&y\geq 0 \end{aligned} ymins.t.Q(x,ξ(w))=pyv(xD)+yxyDy0

Author: Francois Louveaux, John R. Birge Publisher: Springer (2000) Binding: Hardcover, 448 pages pricer: $119.00 ISBN-10: 0387982175 editorialreviews The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The first chapters introduce some worked examples of stochastic programming and demonstrate how a stochastic model is formally built. Subsequent chapters develop the properties of stochastic programs and the basic solution techniques used to solve them. Three chapters cover approximation and sampling techniques and the final chapter presents a case study in depth. A wide range of students from operations research, industrial engineering, and related disciplines will find this a well-paced and wide-ranging introduction to this subject.
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