Assortment optimization with position effects under the nested logit model

We study the assortment optimization problem with position effects under the nested logit model, whose goal is to find the revenue-maximizing subset of products as well as their corresponding display positions. In this joint assortment-position optimization problem, the choices of products are affected by not only their qualities and prices but also the positions where they are displayed. Despite determining the assortment and their corresponding display positions sequentially, we propose to solve this problem in an integrated way to obtain the optimal solution. We formulate this problem as a nonlinear binary integer programming model and develop a dynamic programming based solution approach to obtain the optimal assortment-position assignments. We carry out extensive numerical experiments to evaluate the benefit of our integrated approach. The most important insight we discover is that it is not necessarily better to put the most attractive products in the best position. Moreover, we show that compared to the sequential approaches, our approach can improve revenue by 10.38% on average, which suggests that firms should take into consideration position effects when making assortment decisions. Finally, we discuss results related to two extensions of this problem, that is, the special case when positions are preassigned to nests, and the joint assortment-position-price optimization problem.     

Data-driven research in retail operations—A review

We review the operations research/management science literature on data-driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high-quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state-of-the-art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.     

63A式水陆坦克海上射击优化方法

着眼于63A式水陆坦克海上射击人-机-环境系统的优化,以提高水陆坦克海上射击效能,增强两栖机械化部队渡海登岛作战能力为目的,从缩小坦克射击误差、优化弹药使用与控制、实时进行射击修正、科学确定开火距离、充分发挥单车整体威力等5个层次方面,采用定量分析与定性分析相结合的方法,以未来两栖机械化部队渡海登岛作战为背景,系统阐述并提出了63A式水陆坦克海上射击优化的具体方法.     

不同故障模式下的可靠性优化问题

以阀门流体系统为例 ,通过理论分析与数值计算 ,讨论了组成单元具有多个故障模式情形的系统可靠性优化问题 ,对传统的冗余技术作了进一步的发展 ,确立了考虑不同故障模式时可靠性优化的一般模型 ,最后得到了必须根据不同故障模式比率确定配置方式的结论     

中国高等职业教育资源优化配置研究综述

作者通过梳理近些年有关中国高等职业教育资源优化配置方面的文献,总结出国外以及国内对高等职业教育资源配置的研究现状与研究趋势,并着重从高等职业教育资源优化配置的概念,资源配置的方式,资源配置存在的问题及相应的对策研究这几方面对国内现有文献进行整理,归纳与分析,为后续者书写相关文章奠定了基础。     

基于时敏目标打击的优先级排序模型研究

研究了联合空中作战对时敏目标的优先级排序问题。首先分析了影响时敏目标优先级排序的主要因素,并采用层次分析法定量地确定各主要因素权重;然后建立了时敏目标优先级排序的模糊优化模型;最后通过应用案例仿真,其结果验证了模型的有效性。     

What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

神经网络中引入数值积分寻优法

本文提出了一种新的寻优方法———数值积分寻优法 ,将此方法应用于神经网络的学习算法中 ,构造了两个神经网络 ,它们与BP网络有相同功能 ,且不出现BP网络的局部极小问题 ,收敛速度比BP网快     

基于专家系统的消磁电流决策

分析了建立消磁电流决策的专家系统的可行性 ,从基本概念和实际应用等方面介绍了基于专家系统的消磁电流决策的状态描述、数据库的建立以及推理机的确立等     

Models for production planning under power interruptions

We present a robust optimization model for production planning under the assumption that electricity supply is subject to uncertain interruptions caused by participation in interruptible load contracts (ILCs). The objective is to minimize the cost of electricity used for production while providing a robust production plan which ensures demand satisfaction under all possible interruption scenarios. The combinatorial size of the set of interruption scenarios makes this a challenging problem. Furthermore, we assume that no probabilistic information is known about the supply uncertainty: we only use the information given in the ILC to identify an uncertainty set that captures the possible scenarios. We construct a general robust framework to handle this uncertainty and present a heuristic to compute a good feasible solution of the robust model. We provide computational experiments on a real‐world example and compare the performance of an exact solver applied to the robust model with that of the heuristic procedure. Finally, we include the operational impact of interruptions such as “recovery modes” in the definition of the uncertainty set. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013