ESTIMATING THE VALUE OF WATER AMONG REGIONAL ECONOMIC SECTORS USING THE 1972 NATIONAL INTERINDUSTRY FORMAT1

ABSTRACT: Recent growth in irrigated agriculture and metropolitan areas of the Western United States suggests that if growth continues, there will be a potential shortage in future water resources. Compound the expansion of agriculture and municipal demands with possible enaction of instream flow requirements, a method to allocate water among alternative economic sectors in the West is needed. In this study, an interindustry-linear programming model based on the 1972 national model format is used to allocate scarce water supplies among competing regional economic sectors. The shadow price of water and the sectorial value of water at different availabilities for each competing sector is derived. Also, economic sectors are ranked by value of water at different water availabilities.     

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.     

Solving the probabilistic reserve selection problem

The Reserve Selection Problem consists in selecting certain sites among a set of potential sites for biodiversity protection. In many models of the literature, the species present and able to survive in each site are supposed to be known. Here, for every potential site and for every species considered, only the probability that the species survives in the site is supposed to be known. The problem to select, under a budgetary constraint, a set of sites which maximizes the expected number of species is known in the literature under the name of probabilistic reserve selection problem. In this article, this problem is studied with species weighting to deal differently with common species and rare species. A spatial constraint is also considered preventing to obtain too fragmented reserve networks. As in Polasky et al. (2000), the problem is formulated by a nonlinear mathematical program in Boolean variables. Camm et al. (2002) developed a mixed-integer linear programming approximation that may be solved with standard integer programming software. The method gives tight approximate solutions but does not allow to tell how far these solutions are from the optimum. In this paper, a slightly different approach is proposed to approximate the problem. The interesting aspect of the approach, which also uses only standard mixed-integer programming software, is that it leads, not only to an approximate solution, but also to an upper limit on the true optimal value. In other words, the method gives an approximate solution with a guarantee on its accuracy. The linear reformulation is based on an upper approximation of the logarithmic function by a piecewise-linear function. The approach is very effective on artificial instances that include up to 400 sites and 300 species. Within an average CPU time of about 12 min, near-optimal solutions are obtained with an average relative error, in comparison to the optimum, of less than 0.2%.     

不确定性机会约束无限混合整数优化模型在固体废物管理中的应用

为了更切实有效地反映固体废物管理系统中的多重不确定性,将能够解决区间参数、功能性参数以及随机参数不确定性问题的优化方法相结合,建立了不确定性机会约束无限混合整数优化（IFICCMILP）模型。为了对比说明所建模型的适用性及特点,采用传统的不确定性混合整数规划(IMILP)模型与IFICCMIP模型进行求解。结果表明,由于新建模型输入参数的多样性以及严格的约束条件,其优化结果与IMILP模型有较大区别。传统模型只需要满足一个目标函数,而且约束条件的个数较新建模型少,所得的系统成本较低。但是,因为新建模型能够保证优化结果满足所有的目标函数和约束条件,具有更高的系统可靠性,可以避免由于少考虑约束条件和其他参数而造成的系统风险。此外,新建模型还提供了不同风险水平的优化方案,为决策者提供了更多的决策空间。     

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。     

Counterpart Models in Facility Location Science and Reserve Selection Science

Five classes of zero–one programming models for discrete facility location problems are compared to counterpart models for the selection of conservation reserves. The basic problem of siting facilities to cover demand for services is analogous to the problem of selecting reserves to support species diversity. The classes of models include the set covering and maximal covering models, as well as models for backup and redundant coverage. Issues of reliability and uncertainty are addressed by chance constrained covering models and maximal expected covering models. Exact and heuristic solution approaches are discussed. Multi-objective and economic issues are considered.     

A 'Principled' Approach to Complex Emergencies: Testing a New Aid Delivery Model in the Nuba Mountains

This paper provides an analysis of the Nuba Mountains Programme Advancing Conflict Trans-formation (NMPACT) as an example of an operational response in a complex emergency that innovatively addressed an incipient food security crisis. NMPACT is notable for having brought together an array of actors around a common principled agenda and for being the only operational programme in the Sudan to which both warring parties subscribed during the conflict. The key features of the programme are presented and the main innovative elements are reviewed, including the role of the principles of engagement and the 'political humanitarianism' of NMPACT. The paper looks at how NMPACT broke from traditional externally driven responses to food insecurity, and, drawing on lessons from Operation Lifeline Sudan, adopted an approach that focuses on capacity building, sustainable agriculture and market revitalisation, alongside conflict transformation and peace-building. The limitations of the model are also assessed, and preliminary lessons regarding its replication in other complex emergency contexts are presented.