Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

装备战场损伤的解析分析方法研究

为了提高装备战损模拟的效率,以蒙特卡洛方法为基础,构建了关于装备战损的解析模型。首先采用序贯法优化了仿真次数,以便以较少的仿真次数获得较高的仿真精度;分析了单因素对于装备损伤的影响,并建立了一元回归模型;采用正交试验分析了多因素之间的交互作用,并综合一元回归模型得出了装备战损的多元回归模型;最后结合弹着点分布模型与多元回归模型,建立了装备战损的解析模型,并通过实例验证了解析模型的正确性。     

潜艇运动对被动声纳浮标搜索的影响

由于被动声纳浮标隐蔽持续探测,潜艇可能由于机动进入浮标探测范围,从而增加了被探测概率。首先,在分析已有模型局限性的基础上,从潜艇运动对浮标搜索影响的原理出发,建立单枚浮标新增搜索区域模型,分析了单枚浮标搜索概率的影响因素,然后建模分析了潜艇运动对浮标拦截线搜索概率的影响因素,得出了影响作用仅对浮标作用距离、间距和航向敏感的结论,并对作战使用方法提出相关建议。     

面向无人环境的机器人生态圈自主任务决策方法

借鉴自然界生态系统的典型特征,提出机器人生态圈概念。通过使集群机器人进行智能协同与复杂演化,涌现自我维持、自我复制与自我进化等生命特征,实现无人条件下的长期生存、繁衍与进化,并执行特定的任务。针对机器人生态圈典型任务场景的自主任务决策需求,分析不同机器学习任务决策方法的特点,建立机器人生态圈自主任务决策的决策树模型和神经网络模型。分析表明,两种模型的正确率均在80%~90%,且均具有良好的稳定性。这说明,机器人生态圈自主任务决策问题可以通过决策树、神经网络等机器学习方法来很好地加以解决,从而为面向无人化场景的任务应用提供技术支持。     

基于飞蛾信息素寻偶机制的无人机集群协同搜索

为了提高无人机集群协同搜索移动目标的效率,提出一种基于飞蛾信息素寻偶机制的无人机集群协同搜索方法。根据飞蛾基于信息素选择飞行方向的寻偶行为,建立信息素图风向模型和飞蛾信息素寻偶模型。考虑无人机机间避撞约束,提出从飞蛾信息素寻偶机制到无人机集群分布式协同搜索的映射,并给出具体实现流程。仿真实验结果表明了所提方法在解决单个移动目标的协同搜索问题时的有效性和稳定性;外场飞行试验表明了所提方法在实际应用中的可行性。     

基于Monte Carlo的军事物流结点可靠性评估方法

军事物流网络结点是构成军事物流网络的基本要素。战场环境瞬息万变,结点的可靠性往往受许多不确定因素的影响,计及不确定因素的评估方法可以更客观、真实地评估结点的可靠性。基于此,提出了刻画物流结点可靠性的评估指标体系,建立了计及不确定因素的军事物流结点可靠性评估模型,给出了Monte Carlo求解算法。对影响结点可靠性的因素进行了分析,在此基础上提出了改善军事物流结点可靠性的措施。以某战役级军事物流网络为例进行算例分析,验证了该方法的可行性和正确性。     

基于贝叶斯网络的我国海上能源通道海盗袭击风险分析与实验评估

鉴于能源进口安全保障的重要性及迫切性,针对海上能源通道海盗袭击事件的不确定性与突发性,基于风险理论、贝叶斯网络、蒙特卡洛模拟等方法,建立了海上能源通道海盗袭击风险的量化评估模型,探索了快速、量化评估突发性事件的技术途径,旨在为保障我国能源进口安全提供时效性强、精确度高的决策依据。     

空中进攻作战出动计划禁忌搜索优化分析方法

空中进攻作战出动计划涉及多种复杂因素,大规模空中进攻作战出动计划不仅计算量大、出动强度高、架次多,而且当战场形势发生变化时,必须及时对飞机出动计划做出调整,采用手工方式制定和修改作战计划已难以适应未来空中进攻作战的要求。从分析制定空中进攻作战航空兵出动计划所涉及的诸要素出发,提出了一个较为合理的作战出动计划模型,并采用禁忌搜索求解模型的优良解,为实现计算机制定和及时修正空中进攻作战出动计划提出了一种有效的解决方法。     

作战飞机可靠性与维修性仿真模型

通过故障树分析建立了作战飞机仿真逻辑关系。在修正线性同余法的基础上获得了随机数模型。在各设备故障发生状态和修复状态识别的基础上,建立了可靠性维修性仿真模型,确定了作战飞机在仿真过程中的工作状态,并根据故障模式影响分析预计了设备故障对作战飞机的影响。最后以F/A-18为例进行可靠性维修性仿真,获得了实时的故障信息和修复信息,验证了仿真模型的可行性。