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中国工业与应用数学学会会刊
主管:中华人民共和国教育部
主办:西安交通大学
ISSN 1005-3085  CN 61-1269/O1

工程数学学报 ›› 2020, Vol. 37 ›› Issue (1): 1-15.doi: 10.3969/j.issn.1005-3085.2020.01.001

• •    下一篇

二阶随机占优约束下考虑订购能力的多产品报童问题

付永彬,  孙海琳   

  1. 南京师范大学数学科学学院,南京 210023
  • 收稿日期:2017-12-04 接受日期:2018-05-15 出版日期:2020-02-15 发布日期:2020-04-15
  • 通讯作者: 孙海琳 E-mail: hlsun@njust.edu.cn
  • 基金资助:
    国家自然科学基金(11571178; 11571056).

Multi-product Newsvendor Problem with Constraints of Second Order Stochastic Dominance and Order Capability

FU Yong-bin,  SUN Hai-lin   

  1. School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023
  • Received:2017-12-04 Accepted:2018-05-15 Online:2020-02-15 Published:2020-04-15
  • Contact: H. Sun. E-mail address: hlsun@njust.edu.cn
  • Supported by:
    The National Natural Science Foundation of China (11571178; 11571056).

摘要: 在不确定环境下的报童问题中,利用风险度量去对抗未来的不确定性,是帮助决策者规避风险的重要方法.二阶随机占优(Second order Stochastic Dominance, SSD)是一种稳健的风险度量.本文首先提出一种带有 SSD 约束及订购能力约束的风险厌恶多产品报童模型(SSD 模型).其次,采用样本均值逼近(Sample Average Approximation, SAA)方法近似该问题,并对 SAA 问题进行收敛性分析.最后,在数值实验部分,用切平面法求解 SAA 问题,并同时与基于风险中性(无风险约束)和风险厌恶(以方差为风险约束)假设下的参照模型进行比较.数值结果表明相对于参照模型,在样本外预测下 SSD 模型可以更好地规避风险,得到更高的收益.

关键词: 报童问题, 二阶随机占优, 订购能力, 切平面法, 样本均值逼近

Abstract: Under on uncertain environment, using risk measure to resist the future uncertainties is an important way to avoid risks in newsvendor problem. Second order Stochastic Dominance (SSD) can be used as a robust risk measure. In this paper, we propose a risk aversion multi-product newsvendor model (SSD model) with SSD constraints and capability constraint. Moreover, the sample average approximation (SAA) method is used to approximate the problem, and the convergence analysis of the SAA problem is studied. Finally, in the numerical experiments section, we use the cutting plane method to solve SAA problem, at the same time it is compared with the reference models based on risk neutral (without risk constraints) and risk aversion (with variance as risk constraints). The numerical results show that the SSD model can avoid risks better and get higher return than the reference models under out-of-sample forecast.

Key words: newsvendor problem, second order stochastic dominance, capability, cutting plane method, sample average approximation

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