基于两阶段资源分配协调机制的分布式多项目随机调度

企业趋向于多项目共享全局资源的分布式协同管理。但在多项目实际执行时，全局资源可用量往往由于外部环境的动态变化而存在不确定性，活动中断、资源浪费等现象频发，项目管理变得愈加复杂。本文将不确定的全局资源可用量建模为随机变量，设计两阶段资源分配协调机制，在预分配阶段，考虑项目允许的最大活动中断次数约束，建立各项目调度的马尔可夫动态决策过程模型；预分配结束后，基于活动重要度依次对剩余全局资源进行协调再分配，以提高资源利用率并减少平均项目延期。设计基于全局资源协调分配的Rollout近似动态规划算法进行求解。开展问题库算例实验研究与案例分析，验证协调机制与求解算法的性能；同时，探讨并分析不确定参数对目标结果的影响。     

超视距空战中的多无人机武器目标分配方法

武器目标分配问题是多无人机超视距空战中的关键决策问题之一。本文考虑超视距空战的强对抗、不确定等特点给武器目标分配问题带来的新挑战，基于纳什均衡博弈思想，将超视距空战中的多无人机武器目标分配问题建模为双矩阵博弈模型，其中，基于证据理论设计多级信息融合方法计算多个无人机对抗多个目标时的总攻击有效性。在此基础上，基于纳什均衡中策略被选择的概率与遗憾值之间的关系以及超视距空战需求，将双矩阵博弈模型转换为混合整数规划模型进行求解，并通过典型案例、数值实验、仿真实验对本文方法的计算过程和有效性进行分析。结果表明，本文方法能够有效给出超视距空战中的多无人机武器目标分配方案。     

无人仓系统储位分配问题的优化模型与算法

考虑到无人仓系统补货阶段货架上只有部分空余储位的特点，研究了补货商品储位分配问题的优化模型与算法。以同一货架上存放的商品之间关联度之和最大化为目标建立了混合整数规划模型；结合贪婪算法和邻域搜索算法设计了求解模型的两阶段方法。第一阶段利用贪婪算法求初始可行解；第二阶段利用邻域搜索算法对初始可行解进行优化。利用一个具体算例验证了邻域搜索算法的优化效果，结果显示，通过邻域搜索算法对初始可行解的优化，可以使目标函数值至少提升27%。进一步利用多个小规模算例分析了两阶段算法的近似比和求解速度，验证了算法的快速有效性。本文的研究结果不仅解决了货架初始状态非空情况下的储位分配问题，同样适合解决货架初始状态为空的情况，因此更加符合实际场景，可以作为无人仓管理信息系统的核心模型和算法。     

基于收益管理的邮轮客舱分配与定价模型

基于收益管理的思想对邮轮客舱分配与定价问题进行了研究。结合邮轮运营中的个性化特点，例如消费者团体人员构成多样、较长的预售周期以及救生位和儿童看护人员的数量限制等。在不失一般性的前提下，建立了整数规划模型用以确定在预售周期内的不同预售阶段中各种客舱类型的待售数量及其价格，以达到使邮轮公司收益最大化的目的。实验分析表明，该模型在实际应用中是有效的且呈现出显著的年增长趋势，可明显提高邮轮公司的收益。此外，设计了一种基于韦伯分布的EM算法用以解决模型中涉及到的需求量的无约束估计问题。数值算例研究表明，该算法收敛速度快且无约束估计过程可靠有效。     

医院岗位绩效考核平衡计分卡指标的设计

公立医院的社会公益性质要求医院建立一套既突出岗位价值与贡献和工作效率，又避免以追求创收为目的、经济指标为主的岗位薪酬考核体系。综合运用现代人力资源理论和管理科学理论、结合我国公立医院薪酬体系的现状，设计一套系统、科学有效的按岗取酬、按服务质量和工作效率取酬的岗位考核平衡计分卡指标。运用双层规划法将业务部门的指标考核联系到职能部门，应用层次分析法辅助确认指标权重。     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

Statistical inference for Cox proportional hazards models with a diverging number of covariates

For statistical inference on regression models with a diverging number of covariates, the existing literature typically makes sparsity assumptions on the inverse of the Fisher information matrix. Such assumptions, however, are often violated under Cox proportion hazards models, leading to biased estimates with under-coverage confidence intervals. We propose a modified debiased lasso method, which solves a series of quadratic programming problems to approximate the inverse information matrix without posing sparse matrix assumptions. We establish asymptotic results for the estimated regression coefficients when the dimension of covariates diverges with the sample size. As demonstrated by extensive simulations, our proposed method provides consistent estimates and confidence intervals with nominal coverage probabilities. The utility of the method is further demonstrated by assessing the effects of genetic markers on patients' overall survival with the Boston Lung Cancer Survival Cohort, a large-scale epidemiology study investigating mechanisms underlying the lung cancer.     

The Design of Feature‐Limited Demonstration Software: Choosing the Right Features to Include

Today, software supports many important tasks in a variety of industries. In the specialized nature of these environments, a common problem faced by software vendors is to correctly signal the true value of a software product to the end users. For example, telecommunications equipment manufacturers design complex software for important functions like provisioning new users in the network. These software products automate various functions that would otherwise need to be done manually. In order to enable potential customers—telecommunications providers—to evaluate and recognize the full value of the product, equipment vendors often provide a free, feature‐limited version of the product to the customer. As the specific features included in the feature‐limited version influence whether the full product is purchased or not, it is essential that the features included in the feature‐limited version be selected judiciously. While the importance of identifying the best set of features has been well recognized, there has been little research to date that systematically addresses this fundamental business decision. This study fills this gap in the literature by providing an objective approach to the design of demonstration software. We illustrate the benefits of our approach through a case study involving the design of a feature‐limited demo for a wireless telecommunications equipment manufacturer.