Evergreening and operational risk under price competition

“Evergreening” is a strategy wherein an innovative pharmaceutical firm introduces an upgrade of its current product when the patent on this product expires. The upgrade is introduced with a new patent and is designed to counter competition from generic manufacturers that seek to imitate the firm's existing product. However, this process is fraught with uncertainty because the upgrade is subject to stringent guidelines and faces approval risk. Thus, an incumbent firm has to make an upfront production capacity investment without clarity on whether the upgrade will reach the market. This uncertainty may also affect the capacity investment of a competing manufacturer who introduces a generic version of the incumbent's existing product but whose market demand depends on the success or failure of the upgrade. We analyze a game where capacity investment occurs before uncertainty resolution and firms compete on prices thereafter. Capacity considerations that arise due to demand uncertainty introduce new factors into the evergreening decision. Equilibrium analysis reveals that the upgrade's estimated approval probability needs to exceed a threshold for the incumbent to invest in evergreening. This threshold for evergreening increases as the intensity of competition in the generic market increases. If evergreening is optimal, the incumbent's capacity investment is either decreasing or nonmonotonic with respect to low end market competition depending on whether the level of product improvement in the upgrade is low or high. If the entrant faces a capacity constraint, then the probability threshold for evergreening is higher than the case where the entrant is not capacity constrained. Finally, by incorporating the risk‐return trade‐off that the incumbent faces in terms of the level of product improvement versus the upgrade success probability, we can characterize policy for a regulator. We show that the introduction of capacity considerations may maximize market coverage and/or social surplus at incremental levels of product improvement in the upgrade. This is contrary to the prevalent view of regulators who seek to curtail evergreening involving incremental product improvement. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 71–89, 2016     

知识的通信资源管理决策支持系统

专家系统可以模拟专家分析和解决问题的过程进行推理,最终给出合理的方案.将人工智能技术引入通信资源管理,提出了基于知识的通信资源管理方法,讨论了基于该方法的通信资源管理决策支持系统体系结构、知识库资源优化和推理机制.     

战场环境中多无人机任务分配的快速航路预估算法

战场环境中多无人机任务分配的航路预估是一个具有多路径和实时性双重要求的路径规划问题。采用概率路标图方法对多无人机多任务的航路预估问题开展研究,将航路预估分为离线学习和在线查询两个阶段。通过将战场中威胁的影响转化为各路标间航段的风险代价,提出了基于代价变换的概率路标图方法,当战场态势发生变化时,在不需重构路标图的条件下可以通过局部航段风险代价的调整快速规划出新的预估航路。根据规划条件采取不同的采样策略,可以在规划时间和航路质量之间实现协调以满足不同的战术要求。仿真结果表明该方法是一种快速有效的航路预估方法。     

光互连网络中资源预留的多级光互连仲裁机制

基于资源预留策略提出一种多级光互连仲裁机制,通过将网络分级实现快速、高效的仲裁。多优先级数据缓存队列的传输节点设计,提供了不同类型流量的差异化传输;通过预约式两级仲裁机制,实现网络的完全公平与100%的高吞吐率。设计并对快速仲裁通道进行了合理布局,极大地缩短了仲裁延迟。仿真结果表明:采用基于资源预留的分级仲裁策略,在多种流量模式下所有节点均获得公平的服务。与Feather Weight相比,分级仲裁策略吞吐率提高17%;与2-pass相比,仲裁延迟减少15%,同时,功耗减少5%。     

RCS特性辅助的CMIMO雷达功率资源分配方法

针对集中式多输入多输出(collocated multiple-input multiple-output, CMIMO)雷达在实际探测过程中,未有效结合目标雷达散射截面(radar cross section, RCS)高动态变化特性导致雷达跟踪精度不高甚至失跟的问题,提出一种基于目标RCS高动态特性的CMIMO雷达功率资源自适应分配方法。考虑到目标RCS特征的角度敏感性,利用目标运动状态的可预测性动态获取实际观测角度,从而完成跟踪帧的极化方式优选;在此基础上构建包含功率与RCS的多目标跟踪误差后验克拉美罗下界,将其作为目标函数进行优化,利用内点惩罚法求解该凸优化问题即可实现RCS高动态情况下的功率优化分配。实验结果表明:该方法可结合目标不同方向RCS动态分集特性实现功率的有效分配,相比于传统RCS模型分配方案,有效解决了分配方案与实际跟踪场景之间的失配问题,从而提升了CMIMO雷达的多目标跟踪性能。     

A bicriterion approach to common flow allowances due window assignment and scheduling with controllable processing times

We investigate a single‐machine scheduling problem for which both the job processing times and due windows are decision variables to be determined by the decision maker. The job processing times are controllable as a linear or convex function of the amount of a common continuously divisible resource allocated to the jobs, where the resource allocated to the jobs can be used in discrete or continuous quantities. We use the common flow allowances due window assignment method to assign due windows to the jobs. We consider two performance criteria: (i) the total weighted number of early and tardy jobs plus the weighted due window assignment cost, and (ii) the resource consumption cost. For each resource consumption function, the objective is to minimize the first criterion, while keeping the value of the second criterion no greater than a given limit. We analyze the computational complexity, devise pseudo‐polynomial dynamic programming solution algorithms, and provide fully polynomial‐time approximation schemes and an enhanced volume algorithm to find high‐quality solutions quickly for the considered problems. We conduct extensive numerical studies to assess the performance of the algorithms. The computational results show that the proposed algorithms are very efficient in finding optimal or near‐optimal solutions. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 41–63, 2017     

测试性增长中资源优化配置模型及求解

良好的测试性设计对系统维修性具有重要意义,测试性增长试验通过一系列测试性设计缺陷发现和纠正措施,可保证系统测试性指标达到设计要求。针对基于延缓纠正的测试性增长过程中的资源配置问题进行研究,基于增长试验目标是否明确和试验资源是否受限制问题构建资源优化配置模型,并提出一种基于拉格朗日松弛和本地搜索的快速优化算法。仿真结果表明:该模型能够有效指导测试性增长中的资源优化配置问题,所提混合优化方法能够高效、准确地求解整数规划问题。     

子弹均匀散布子母弹最有利火力分配方法

在将子弹均匀散布的椭圆区域等效转换为矩形区域的基础上,建立了适宽射向射击子母弹对矩形目标毁伤全概率计算的数学模型。通过函数转换和泛函分析给出了子弹均匀散布子母弹理想射击密度,得到了理想射击密度下对目标的毁伤概率计算公式。讨论了最有利火力分配方式的确定方法,给出了最优射向间隔和表尺差的计算公式,为便于实际应用,给出了最优火力分配计算中所需的3个参数的近似计算公式,最后给出了应用算例。     

军事云环境下基于动态博弈的资源调度方法

针对军事云环境下联合指挥资源调度优化问题,综合考虑了多部队任务需求,多目标优化,多部队资源竞争等约束条件,运用动态博弈理论及方法,建立并提出了一种基于完全信息扩展博弈的资源调度模型及方法,提高了资源调度效率,兼顾了多部队利益,增加了联合作战效益。最后通过实验验证了其有效性及准确性。     

A self‐adapting genetic algorithm for project scheduling under resource constraints

This papers deals with the classical resource‐constrained project scheduling problem (RCPSP). There, the activities of a project have to be scheduled subject to precedence and resource constraints. The objective is to minimize the makespan of the project. We propose a new heuristic called self‐adapting genetic algorithm to solve the RCPSP. The heuristic employs the well‐known activity list representation and considers two different decoding procedures. An additional gene in the representation determines which of the two decoding procedures is actually used to compute a schedule for an individual. This allows the genetic algorithm to adapt itself to the problem instance actually solved. That is, the genetic algorithm learns which of the alternative decoding procedures is the more successful one for this instance. In other words, not only the solution for the problem, but also the algorithm itself is subject to genetic optimization. Computational experiments show that the mechanism of self‐adaptation is capable to exploit the benefits of both decoding procedures. Moreover, the tests show that the proposed heuristic is among the best ones currently available for the RCPSP. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 433–448, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10029