基于熵权的装备保障辅助决策系统效能评判

装备保障辅助决策系统是辅助装备保障指挥员顺利完成装备保障任务的重要手段,其效能优劣极大地影响到作战结果.在辅助决策系统的概念基础上,分析了装备保障指挥的特点,建立了包含决策的合理性、系统的时效性、硬件可靠性等十个指标的评判体系,利用熵权法得出评判指标体系的熵权,给出了系统的综合评判模型.最后通过实例验证了该模型的可行性和实用性.     

Optimal production ramp-up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp-up model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.     

Revenue maximization in two-station tandem queueing systems

We study optimal pricing for tandem queueing systems with finite buffers. The service provider dynamically quotes prices to incoming price sensitive customers to maximize the long-run average revenue. We present a Markov decision process model for the optimization problem. For systems with two stations, general-sized buffers, and two or more prices, we describe the structure of the optimal dynamic pricing policy and develop tailored policy iteration algorithms to find an optimal pricing policy. For systems with two stations but no intermediate buffer, we characterize conditions under which quoting either a high or a low price to all customers is optimal and provide an easy-to-implement algorithm to solve the problem. Numerical experiments are conducted to compare the developed algorithms with the regular policy iteration algorithm. The work also discusses possible extensions of the obtained results to both three-station systems and two-station systems with price and congestion sensitive customers using numerical analysis.     

On the reduction of total‐cost and average‐cost MDPs to discounted MDPs

This article provides conditions under which total‐cost and average‐cost Markov decision processes (MDPs) can be reduced to discounted ones. Results are given for transient total‐cost MDPs with transition rates whose values may be greater than one, as well as for average‐cost MDPs with transition probabilities satisfying the condition that there is a state such that the expected time to reach it is uniformly bounded for all initial states and stationary policies. In particular, these reductions imply sufficient conditions for the validity of optimality equations and the existence of stationary optimal policies for MDPs with undiscounted total cost and average‐cost criteria. When the state and action sets are finite, these reductions lead to linear programming formulations and complexity estimates for MDPs under the aforementioned criteria.© 2017 Wiley Periodicals, Inc. Naval Research Logistics 66:38–56, 2019     

Maintaining systems with heterogeneous spare parts

We consider the problem of optimally maintaining a stochastically degrading, single‐unit system using heterogeneous spares of varying quality. The system's failures are unannounced; therefore, it is inspected periodically to determine its status (functioning or failed). The system continues in operation until it is either preventively or correctively maintained. The available maintenance options include perfect repair, which restores the system to an as‐good‐as‐new condition, and replacement with a randomly selected unit from the supply of heterogeneous spares. The objective is to minimize the total expected discounted maintenance costs over an infinite time horizon. We formulate the problem using a mixed observability Markov decision process (MOMDP) model in which the system's age is observable but its quality must be inferred. We show, under suitable conditions, the monotonicity of the optimal value function in the belief about the system quality and establish conditions under which finite preventive maintenance thresholds exist. A detailed computational study reveals that the optimal policy encourages exploration when the system's quality is uncertain; the policy is more exploitive when the quality is highly certain. The study also demonstrates that substantial cost savings are achieved by utilizing our MOMDP‐based method as compared to more naïve methods of accounting for heterogeneous spares.     

基于多时刻目标运动模式分布的多模型算法

在机动目标跟踪过程中,传感器在利用量测数据计算目标状态的同时,需要完成对目标运动模式的估计,并根据估计结果对传感器滤波方程的参数或结构进行调整,以减小滤波方程与目标运动模式之间的差异,提高对机动目标的跟踪精度.针对已有多模型算法的不足,利用多个时刻的目标运动模式分布来解决混合估计问题,提出一种新的次优多模型(MTMM)算法.仿真结果证明了MTMM算法的有效性.     

Lot sizing with learning and forgetting in setups: Analytical results and insights

This article considers the dynamic lot sizing problem when there is learning and forgetting in setups. Learning in setups takes place with repetition when additional setups are made and forgetting takes place when there is a break between two successive setups. We allow the amount forgotten over a break to depend both on the length of the break and the amount of learning at the beginning of the break. The learning and forgetting functions we use are realistic. We present several analytical results and use these in developing computationally efficient algorithms for solving the problem. Some decision/forecast horizon results are also developed, and finally we present managerial insights based on our computational results. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 93–108, 2016     

装备器材保障资源调度决策模型及算法

装备器材保障资源调度问题是一个非常复杂的问题,根据其优化目标要求,从保障时间最短、保障耗费最低、安全性最高3个方面建立了该问题的多目标优化模型,并通过目标优先度决策将其转化为单目标模型;接着,采用两阶段法进行求解,将其分为最优路径决策、器材分配决策两个阶段进行决策优化,在明确资源点到需求点之间的最优路径后再进行器材资源的分配;并分别采用基于小生境的自适应遗传算法和基于生成树的遗传算法进行求解。通过实例分析,求解结果能够满足装备器材保障的要求,表明所构建的决策模型和算法是有效的。     

兵棋推演：复杂系统管理的创新与实践

复杂系统性质决定了复杂系统管理的特殊性,而兵棋推演可以为复杂系统管理提供更加符合其特性的决策辅助支持。讨论了复杂系统的性质及其影响,研究了兵棋推演用于复杂系统管理的原因和方法,并结合战争兵棋推演,介绍了兵棋工程建设中系统研发、推演组织和工程管理方面的创新与实践问题。最后,给出了未来发展的趋势。     

合成分队火力分配协同决策模型研究

针对合成分队火力分配效率和科学性不高的问题,采用协同决策思想对合成分队火力优化分配方法进行了研究。针对合成分队的作战特点提出了3种模型:建立了攻击力量类型相同的多种准则的火力分配模型,建立了攻击力量类型不同的基于双层规划的火力分配模型,建立了具有上级指定任务的分队内和分队间的火力协同分配模型,并对相关模型进行了实例仿真验证。提出的这3种模型能够解决合成分队在火力分配中的协同决策问题,可提高作战指挥决策的实时性和科学性。