基于马尔科夫链-多目标模型的应急供应链决策优化研究*

为应对洪涝、新冠肺炎疫情等突发灾难,提出供应物资满足率最大、供应时间最短、供应成本最低的离散时间马尔科夫链-多目标规划模型（DTMC-MOP）,动态地识别、分析、应对应急供应链风险;采用改进自适应NSGA-Ⅱ算法求解优化模型,并通过标准测试函数进行测试与评价,验证模型的可行性和有效性;通过算例分析,获得精度更高、分布更均匀的Pareto最优前沿。研究结果表明:决策者可以依据应急管理核心目标或不同偏好选择相适应的应急方案,研究结果可为应急供应链决策优化提供1种科学方法,对保障灾民生命安全、维护社会和谐稳定具有积极意义。     

基于阶次序列与凸规划的铀尾矿库监测定位算法

在铀尾矿库放射性核素泄露污染WSN监测中,针对凸规划定位精度低的问题,提出一种基于阶次序列的凸规划定位算法。通过划分子区域缩小未知节点几何约束范围,建立各子区域的重心坐标到锚节点的阶次序列表,计算未知节点序列与序列表的相关度,并以最大相关度的子区域重心坐标为定位结果。使该改进算法满足低密度条件的定位并能减少其计算量提高定位精度。经过仿真,新算法较凸规划算法在定位精度上提高了14%。     

Livestock mortality in pastoralist herds in Ethiopia and implications for drought response

Participatory epidemiology methods were employed retrospectively in three pastoralist regions of Ethiopia to estimate the specific causes of excess livestock mortality during drought. The results showed that starvation/dehydration accounted for between 61.5 and 100 per cent of excess livestock mortality during drought, whereas disease‐related mortality accounted for between 0 and 28.1 per cent of excess mortality. Field observations indicate that, in livestock, disease risks and mortality increase in the immediate post‐drought period, during rain. The design of livelihoods‐based drought response programmes should include protection of core livestock assets, and it should take account of the specific causes of excess livestock mortality during drought and immediately afterwards. This study shows that, when comparing livestock feed supplementation and veterinary support, relatively more aid should be directed at the former if the objective is to protect core livestock during drought. Veterinary support should consider disease‐related mortality in the immediate post‐drought period, and tailor inputs accordingly.     

基于响应场的线性规划方法在长江口总量分配计算中的应用

河口区污染源总量分配由于计算的复杂性而受到较多的关注. 以长江口及毗邻海域为例,研究基于响应场的线性规划方法在污染源总量分配计算中的应用. 主要步骤:通过水动力水质模型计算各污染源在单位负荷下的响应场,建立污染源与水质之间的响应关系;构建总量分配计算的优化目标和约束方程,采用线性规划方法,计算污染物环境容量;考虑污染源之间的公平性原则,进行污染源总量分配. 选取有代表性的水文设计条件,完成对COD_Mn,NH₃-N,无机氮和活性磷酸盐的总量分配计算. 在给定的设计水量、水质目标、排污口位置和分配原则等条件下,得到COD_Mn,NH₃-N,无机氮和活性磷酸盐的最大允许入海量分别为200.8×104,16.9×104,22.6×104和1.8×104 t.      

不确定性城市生活垃圾管理规划及其应用

通过对城市生活垃圾管理过程中的经济成本和环境影响分析,建立了机会约束区间不确定性线性规划模型(ICILP). 将该模型应用于北京市海淀区城市生活垃圾管理规划,并在不同违反概率下进行求解分析. 结果表明:处理设施容量约束的违反概率越大,焚烧和堆肥在垃圾处理中所占比例越大,但系统成本并未随之增加. 提出了海淀区新的城市生活垃圾处理方案,即在现有填埋处理的基础上增加焚烧和堆肥2种处理设施,使城市生活垃圾流量逐渐由填埋转向焚烧和堆肥. 该模型的优化结果虽然会在一定程度上导致系统成本的增加,但是填埋场的寿命被有效延长,并且达到了减小环境污染的目的,从长远的角度看是可行的.      

Risk aversion and adaptive management: Insights from a multi-armed bandit model of invasive species risk

This article explores adaptive management (AM) for decision-making under environmental uncertainty. In the context of targeting invasive species inspections of agricultural imports, I find that risk aversion increases the relative value of AM and can increase the rate of exploratory action. While calls for AM in natural resource management are common, many analyses have identified modest gains from this approach. I analytically and numerically examine the distribution of outcomes from AM under risk neutrality and risk aversion. The inspection decision is framed as a multi-armed bandit problem and solved using the Lagrangian decomposition method. Results show that even when expected gains are modest, asymmetry in the distribution of outcomes has important implications. Notably, AM can serve to buffer against large losses, even if the most likely outcome is a small loss.     

基于动态实验的玻璃钢原油管道结垢规律研究

为了研究玻璃钢管道在原油输送过程中的结垢问题,通过搅拌实验的方法对玻璃钢管道内的结垢情况进行了模拟实验,并运用Origin8.0绘图软件拟合得到了基于油品温度、流量以及流动距离的玻璃钢管道原油输送结垢计算公式;采用VB6.0编程语言,对所研究的内容及公式进行编程,通过与新疆HK玻璃钢管线现场清管运行参数对比,对结垢计算公式进行了修正。结果表明:由该程序计算所得玻璃钢管线结垢量情况以及不同位置在不同流量下的结构速率与实验结果吻合,因此,所编程序计算结果能够有效地为玻璃钢管线现场清管周期的确定提供理论依据。     

Conservation decision-making in large state spaces

When looking for the best course of management decisions to efficiently conserve metapopulation systems, a classic approach in the ecology literature is to model the optimisation problem as a Markov decision process and find an optimal control policy using exact stochastic dynamic programming techniques. Stochastic dynamic programming is an iterative procedure that seeks to optimise a value function at each timestep by evaluating the benefits of each of the actions in each state of the system defined in the Markov decision process.Although stochastic dynamic programming methods provide an optimal solution to conservation management questions in a stochastic world, their applicability in metapopulation problems has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. The high computational requirements of stochastic dynamic programming methods mean that only simple metapopulation management problems can be analysed. In this paper we overcome the complexity burden of exact stochastic dynamic programming methods and present the benefits of an on-line sparse sampling algorithm proposed by Kearns, Mansour and Ng (2002). The algorithm is particularly attractive for problems with large state spaces as the running time is independent of the size of the state space of the problem. This appealing improvement is achieved at a cost: the solutions found are no longer guaranteed to be optimal.We apply the algorithm of Kearns et al. (2002) to a hypothetical fish metapopulation problem where the management objective is to maximise the number of occupied patches over the management time horizon. Our model has multiple management options to combat the threats of water abstraction and waterhole sedimentation. We compare the performance of the optimal solution to the results of the on-line sparse sampling algorithm for a simple 3-waterhole case. We find that three look-ahead steps minimises the error between the optimal solution and the approximation algorithm. This paper introduces a new algorithm to conservation management that provides a way to avoid the effects of the curse of dimensionality. The work has the potential to allow us to approximate solutions to much more complex metapopulation management problems in the future.     

分区达标控制法求解海域环境容量

提出可考虑二维扩散的分式标控制法用于求解海域环境容量。引进贡献度系数将二维容量问题定义为线性规划问题,使各类水功能区在达到所对应的环境标准的前提下各排污口污染允许排放量之和最大。并给出了用二维扩散模型计算贡献质系数的方法。     

Optimization of gas detector placement considering scenario probability and detector reliability in oil refinery installation

Gas detection system is a critical layer of protection in process safety. Leak scenario probability and detector reliability are two key factors in the optimization of gas detector placement. However, they are easily neglected in previous studies, which may lead to an inaccurate evaluation of the optimization solutions. In this study, a stochastic programming (SP) optimization method is proposed considering these two factors. In order to quantitatively represent the probability of leak scenarios, a complete accident scenario set (CASS) is built combining leak sources and wind fields. Then, the computational fluid dynamics (CFD) method is adopted for consequence modeling of gas dispersion. The Markov model is developed to predict the detector reliability. With the objective of minimal cumulative detection time (MCDT), the SP formulation considering scenario probability and detector reliability (MCDT-SPR) is proposed. By introducing the particle swarm optimization (PSO) algorithm, the optimization formulations can be solved. A case study is investigated on a diesel hydrogenation refining unit. Results validate this approach is promising to improve the detection efficiency. This method is more practical and matching with the actual industrial environment, where the leak scenarios and the detector reliability can change dynamically in real process setting.