共被期刊论文引用了4次
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| 1 | DYNAMIC PRICING UNDER TEMPERATURE CONTROL FOR PERISHABLE FOODS显示文摘Consumers pay more and more attention to the quality of perishable foods,which is mainly affected by storage temperature.This paper presents a dynamic pricing model for perishable foods under temperature control.To maximize the total profit,the optimal price and storage temperature are obtained using Pontryagin's maximum principle.A static pricing model is provided to compare with the dynamic one.It is shown by a numerical example that the dynamic policy can make more revenue than the static one.Moreover,the managerial implications are analyzed and the effectiveness of the proposed method is demonstrated. | Wenwen Liu Wansheng Tang Lin Feng Jianxiong Zhang | 2014 | Journal of Systems Science and Systems Engineering2014，23，3： | 3 |
| 2 | 基于零售商主导的需求与价格相关收益共享契约研究显示文摘本文以一个制造商和一个零售商构成的零售商主导型供应链为研究对象,在考虑供过于求的残值损失和供不应求时可以追加订货情况下,研究了需求受零售价格影响时的收益共享契约。结果显示零售商可以利用自身的优势地位和较高的决策权来控制,引导处于跟随者地位的供应商,通过恰当地设计契约条款实现该零售商主导型供应链的协调。 | 张红 万莹洁 黄海明 | 2014 | 商业研究2014，，10： | 3 |
| 3 | Item-level Forecasting for E-commerce Demand with High-dimensional Data Using a Two-stage Feature Selection Algorithm显示文摘With the rapid development of information technology and fast growth of Internet users,e-commerce nowadays is facing complex business environment and accumulating large-volume and highdimensional data.This brings two challenges for demand forecasting.First,e-merchants need to find appropriate approaches to leverage the large amount of data and extract forecast features to capture various factors affecting the demand.Second,they need to efficiently identify the most important features to improve the forecast accuracy and better understand the key drivers for demand changes.To solve these challenges,this study conducts a multi-dimensional feature engineering by constructing five feature categories including historical demand,price,page view,reviews,and competition for e-commerce demand forecasting on item-level.We then propose a two-stage random forest-based feature selection algorithm to effectively identify the important features from the high-dimensional feature set and avoid overfittlng.We test our proposed algorithm with a large-scale dataset from the largest e-commerce platform in China.The numerical results from 21,111 items and 109 million sales observations show that our proposed random forest-based forecasting framework with a two-stage feature selection algorithm delivers 11.58%,5.81%and 3.68%forecast accuracy improvement,compared with the Autoregressive Integrated Moving Average(ARIMA),Random Forecast,and Random Forecast with one-stage feature selection approach,respectively,which are widely used in literature and industry.This study provides a useful tool for the practitioners to forecast demands and sheds lights on the B2C e-commerce operations management. | Hongyan Dai Qin Xiao Nina Yan Xun Xu Tingting Tong | 2022 | Journal of Systems Science and Systems Engineering2022，31，2： | 1 |
| 4 | 需求受价格影响的三层供应链协调模型显示文摘针对由一个制造商、一个批发商和一个零售商构成的三层供应链系统,在随机市场需求且需求受商品零售价格影响的情况下,研究了零售商如何定价和确定订货量,在此基础上给出了一个能使供应链系统达到完美协调的收益分享合约策略,最后给出了数值例子. | 杨爱峰 祖珊珊 | 2011 | 大学数学2011，27，1： | 0 |
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