基于对数模糊优先规划法的火灾风险评估

随着城市化进程的推进,每年都有大量的火灾事故发生,造成大量的人员伤亡和财产损失,为了避免事故发生或者减少伤亡损失,火灾风险评估是一种有效的方法之一。基于层次分析法,提出了对数模糊优先规划法进行火灾风险评估。首先以地下车库火灾风险评估为例,参考相关文献和专家意见,建立了地下车库火灾风险评价指标体系;然后利用对数模糊优先规划法计算各个评价指标的权重,然后根据专家打分获得评语集,结果所计算的权重集,可以得到地下车库的火灾风险评估结果为"良好"。最后,进行讨论了火灾风险评估的方法的有效性,并与层次分析法对比,对数模糊优先规划法可以使得决策者更加自信和舒适,并且决策结果更加合理。     

An inexact optimization approach for river water-quality management

A previously developed fuzzy waste load allocation model (FWLAM) for a river system is extended to address uncertainty involved in fixing the membership functions for the fuzzy goals of the pollution control agency (PCA) and the dischargers using the concept of grey systems. The model provides flexibility for the PCA and the dischargers to specify their goals independently, as the parameters for membership functions are considered as interval grey numbers instead of deterministic real numbers. An inexact or a grey fuzzy optimization model is developed in a multiobjective framework, to maximize the width of the interval valued fractional removal levels for providing latitude in decision-making and to minimize the width of the goal fulfillment level for reducing the system uncertainty. The concept of an acceptability index for order relation between two partially or fully overlapping intervals is used to get a deterministic equivalent of the grey fuzzy optimization model developed. The improvement of the optimal solutions over a previously developed grey fuzzy waste load allocation model (GFWLAM) is shown through an application to a hypothetical river system. The fuzzy multiobjective optimization and fuzzy goal programming techniques are used to solve the deterministic equivalent of the GFWLAM.     

Siting and Routing Assessment for Solid Waste Management Under Uncertainty Using the Grey Mini-Max Regret Criterion

Solid waste management (SWM) is at the forefront of environmental concerns in the Lower Rio Grande Valley (LRGV), South Texas. The complexity in SWM drives area decision makers to look for innovative and forward-looking solutions to address various waste management options. In decision analysis, it is not uncommon for decision makers to go by an option that may minimize the maximum regret when some determinant factors are vague, ambiguous, or unclear. This article presents an innovative optimization model using the grey mini-max regret (GMMR) integer programming algorithm to outline an optimal regional coordination of solid waste routing and possible landfill/incinerator construction under an uncertain environment. The LRGV is an ideal location to apply the GMMR model for SWM planning because of its constant urban expansion, dwindling landfill space, and insufficient data availability signifying the planning uncertainty combined with vagueness in decision-making. The results give local decision makers hedged sets of options that consider various forms of systematic and event-based uncertainty. By extending the dimension of decision-making, this may lead to identifying a variety of beneficial solutions with efficient waste routing and facility siting for the time frame of 2005 through 2010 in LRGV. The results show the ability of the GMMR model to open insightful scenario planning that can handle situational and data-driven uncertainty in a way that was previously unavailable. Research findings also indicate that the large capital investment of incineration facilities makes such an option less competitive among municipal options for landfills. It is evident that the investment from a municipal standpoint is out of the question, but possible public–private partnerships may alleviate this obstacle.     

Comparison of Two Spatial Optimization Techniques: A Framework to Solve Multiobjective Land Use Distribution Problems

Two spatial optimization approaches, developed from the opposing perspectives of ecological economics and landscape planning and aimed at the definition of new distributions of farming systems and of land use elements, are compared and integrated into a general framework. The first approach, applied to a small river catchment in southwestern France, uses SWAT (Soil and Water Assessment Tool) and a weighted goal programming model in combination with a geographical information system (GIS) for the determination of optimal farming system patterns, based on selected objective functions to minimize deviations from the goals of reducing nitrogen and maintaining income. The second approach, demonstrated in a suburban landscape near Leipzig, Germany, defines a GIS-based predictive habitat model for the search of unfragmented regions suitable for hare populations (Lepus europaeus), followed by compromise optimization with the aim of planning a new habitat structure distribution for the hare. The multifunctional problem is solved by the integration of the three landscape functions (“production of cereals,” “resistance to soil erosion by water,” and “landscape water retention”). Through the comparison, we propose a framework for the definition of optimal land use patterns based on optimization techniques. The framework includes the main aspects to solve land use distribution problems with the aim of finding the optimal or best land use decisions. It integrates indicators, goals of spatial developments and stakeholders, including weighting, and model tools for the prediction of objective functions and risk assessments. Methodological limits of the uncertainty of data and model outcomes are stressed. The framework clarifies the use of optimization techniques in spatial planning.     

Children and fighting forces: 10 years on from Cape Town

It is 10 years since the adoption of the Cape Town Principles and Best Practices on the Prevention of Recruitment of Children into the Armed Forces and on Demobilization and Social Reintegration of Child Soldiers in Africa. The field of programming for the reintegration of children associated with armed forces and armed groups has made significant strides in this period. However, important gaps in the knowledge base remain. This paper examines empirical evidence that supports lessons learned from work with children formerly connected with fighting forces. It evaluates what is known, where promising practice exists, and lacunae in five programming areas: psychosocial support and care; community acceptance; education, training and livelihoods; inclusive programming for all war-affected children; and follow-up and monitoring. While the 2007 Paris Commitments to Protect Children from Unlawful Recruitment or Use by Armed Forces or Groups mark an emerging consensus on many issues, there is still a critical need for more systematic studies to develop the evidence base supporting intervention in this area.     

Optimal planning of co-firing alternative fuels with coal in a power plant by grey nonlinear mixed integer programming model

Energy supply and use is of fundamental importance to society. Although the interactions between energy and environment were originally local in character, they have now widened to cover regional and global issues, such as acid rain and the greenhouse effect. It is for this reason that there is a need for covering the direct and indirect economic and environmental impacts of energy acquisition, transport, production and use. In this paper, particular attention is directed to ways of resolving conflict between economic and environmental goals by encouraging a power plant to consider co-firing biomass and refuse-derived fuel (RDF) with coal simultaneously. It aims at reducing the emission level of sulfur dioxide (SO(2)) in an uncertain environment, using the power plant in Michigan City, Indiana as an example. To assess the uncertainty by a comparative way both deterministic and grey nonlinear mixed integer programming (MIP) models were developed to minimize the net operating cost with respect to possible fuel combinations. It aims at generating the optimal portfolio of alternative fuels while maintaining the same electricity generation simultaneously. To ease the solution procedure stepwise relaxation algorithm was developed for solving the grey nonlinear MIP model. Breakeven alternative fuel value can be identified in the post-optimization stage for decision-making. Research findings show that the inclusion of RDF does not exhibit comparative advantage in terms of the net cost, albeit relatively lower air pollution impact. Yet it can be sustained by a charge system, subsidy program, or emission credit as the price of coal increases over time.     

Optimal Policies for Aggregate Recycling from Decommissioned Forest Roads

To mitigate the adverse environmental impact of forest roads, especially degradation of endangered salmonid habitat, many public and private land managers in the western United States are actively decommissioning roads where practical and affordable. Road decommissioning is associated with reduced long-term environmental impact. When decommissioning a road, it may be possible to recover some aggregate (crushed rock) from the road surface. Aggregate is used on many low volume forest roads to reduce wheel stresses transferred to the subgrade, reduce erosion, reduce maintenance costs, and improve driver comfort. Previous studies have demonstrated the potential for aggregate to be recovered and used elsewhere on the road network, at a reduced cost compared to purchasing aggregate from a quarry. This article investigates the potential for aggregate recycling to provide an economic incentive to decommission additional roads by reducing transport distance and aggregate procurement costs for other actively used roads. Decommissioning additional roads may, in turn, result in improved aquatic habitat. We present real-world examples of aggregate recycling and discuss the advantages of doing so. Further, we present mixed integer formulations to determine optimal levels of aggregate recycling under economic and environmental objectives. Tested on an example road network, incorporation of aggregate recycling demonstrates substantial cost-savings relative to a baseline scenario without recycling, increasing the likelihood of road decommissioning and reduced habitat degradation. We find that aggregate recycling can result in up to 24% in cost savings (economic objective) and up to 890% in additional length of roads decommissioned (environmental objective).     

基于纳污能力控制的省区初始排污权ITSP配置模型

省区初始排污权配置具有多阶段性、复杂性及不确定性特征。面向水功能区限制纳污红线约束,根据省区初始排污权配置的基本假设,引入区间数和随机数来描述不确定性信息,以因省区初始排污权配置产生的经济效益为第1个阶段,以因承担减排责任而可能产生的治污损失为第2个阶段,设计实现流域经济效益最优的目标函数,并以配置结果能够体现社会效益、生态环境效益和社会经济发展连续性为约束条件,构建基于纳污能力控制的省区初始排污权区间两阶段随机规划(ITSP)配置模型,分水污染物类别确定不同减排情形下的省区初始排污权配置方案。在三种减排情形下,2020年太湖流域各省区的初始排污权配置结果表明:1江苏省、浙江省和上海市的COD初始排污权配置区间量没有明显变化,其NH3-N和TP初始排污权配置区间量总体呈上升或递增趋势;2太湖流域各省区因初始排污权的配置产生的总体经济效益最优区间数分别为[335.35,399.75]亿元、[336.63,401.11]亿元和[339.08,402.74]亿元,最优区间数的下限值、上限值及期望值总体呈上升或递增趋势。分类确定不同减排情形下的配置方案,并提出方案实施的政策建议,为排污权配置决策提供更为准确的决策空间。     

突发事故下应急资源优化配置定量模型研究

以提高城市的应急管理能力为目标,针对突发事故灾害应急管理的特点,根据应急点的数目,将应急资源配置过程划分为若干阶段.以每一阶段开始或上一阶段结束时已调度的应急资源数量为状态变量,结合动态规划的基本理论,构建应急资源优化配置数学模型,并通过案例分析对该模型的实用性和有效性进行验证.结果表明,基于动态规划理论的应急资源优化配置定量模型可以避免传统管理模式下应急资源配置过程中的重复、缺项以及资源利用低效等问题,能有效指导应急过程中应急资源的优化配置,提高突发事故下城市的整体应急能力.     

不确定性下区域规划环评方案优化方法框架研究

为了弥补以往规划环评方案中存在的两个缺陷,构建了不确定性条件下融合型和调整型区域规划环评方案优化方法框架,从而消除规划方案本身潜在的环境影响,且实现了规划方案和环境保护补救措施的系统优化及其不确定性风险决策.同时,从方法学上,基于强化区间优化模型建立了不确定性条件下规划方案和补救措施的双层优化方法.最后,针对郑州市土地利用总体规划(1997～2010年)的规划环评,以生态系统服务功能价值的最大化为目标,以郑州市土地资源、经济发展(GDP)和环境指标(土地承载COD排放总量)为约束条件,得到不同风险水平下土地利用规划优化方案.结果表明:①优化后的规划方案的生态系统服务功能价值高于原规划方案,约为原规划方案的1.45～1.52倍,且完全能满足郑州市2010年GDP目标;②有必要在未来融合型或调整型规划环评融入不确定性优化思想,且需要为决策者提供不同环境风险水平下的决策方案.