An interval joint-probabilistic programming method for solid waste management: a case study for the city of Tianjin, China

Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and population growth associated with increased waste generation rates and decreased waste disposal capacities. In response to these challenges, an interval joint-probabilistic mixed-integer programming （IJMP） method is developed for supporting long-term planning of waste management activities in the city of Tianjin, which is one of the largest municipalities in the northern part of China. In the IJMP, joint probabilistic constraints are introduced into an interval-parameter mixed-integer programming framework, such that uncertainties presented in terms of interval values and random variables can be reflected. Moreover, a number of violation levels for the waste-management-capacity constraints are examined, which can facilitate in-depth analyses of tradeoffs among economic objective and system-failure risk. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment of the city＇s existing waste-management practices and the long- term planning of the city＇s waste-management facilities.     

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。     

基于生态系统服务价值的土地利用结构优化——以重庆万州为例

基于中国陆地生态系统服务功能价值研究成果,以及重庆市万州区生态环境建设与经济社会发展水平,对不同土地利用类型单位面积生态服务价值进行修正,形成万州区土地利用结构优化灰色线性规划模型中决策变量的生态经济参数。计算结果表明：（1）规划期内万州区耕地面积略有减少,建设用地面积有所增加,林地面积有较大幅度增加,大规模增加湿地面积提高土地生态功能价值则难以实现;（2）地质灾害与水土流失综合治理、草地生态系统修复以及适当增加园地面积,能够提高万州区土地生态功能价值;（3）万州区土地生态经济理论价值为11888亿元/a,修正价值为10644亿元/a,均高于现状值9653亿元/a,土地生态修复与建设的生态经济效益可观;（4）基于生态系统服务价值的土地利用结构优化,能够满足社会经济发展对土地的需求并同步实现生态经济效益的最优,为解决土地利用规划中的关键技术问题提供新的方法。     

Inexact rough-interval two-stage stochastic programming for conjunctive water allocation problems

An inexact rough-interval two-stage stochastic programming (IRTSP) method is developed for conjunctive water allocation problems. Rough intervals (RIs), as a particular case of rough sets, are introduced into the modeling framework to tackle dual-layer information provided by decision makers. Through embeding upper and lower approximation intervals, rough intervals are capable of reflecting complex parameters with the most reliable and possible variation ranges being identified. An interactive solution method is also derived. A conjunctive water-allocation system is then structured for characterizing the proposed model. Solutions indicate a detailed optimal allocation scheme with a rough-interval form; a total of [[1048.83, 2078.29]:[1482.26, 2020.60]] would be obtained under the pre-regulated inputs. Comparisons of the proposed model to a conventional and an interval two-stage stochastic programming model are also conducted. The results indicate that the optimal objective function values of TSP and ITSP always fall into the range of , while they are sometimes out of the range of ; the optimal solutions of decision variables also present this feature. This implies the reliability of IRTSP in handling conjunctive water allocation problems.     

Sperm allocation in an uncertain world

Models of optimal sperm allocation are constructed using stochastic dynamic programming techniques, yielding predictions about sperm allocation when males mate sequentially, breeding time is finite, sperm stores are limited and females vary in quality. The models suggest that uncertainty of future reproductive opportunities should favour withholding of sperm, so that males tend to allocate less (for a given level of stored sperm) earlier in the breeding season. This effect is more pronounced the greater the variance in female quality. We also show that while allocation will be influenced by mate value, it is not necessarily optimal to allocate preferentially to high-quality females, since the benefits of a higher-quality mate may be offset by increased risk of rejection of sperm or higher sperm competition. The relationship between mate quality and level of allocation will depend strongly on the amount of remaining stored sperm, with males whose supplies are depleted being more likely to favour lower-quality partners. Received: 12 September 1997 / Accepted after revision: 28 June 1998     

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。