An Interval-Parameter Fuzzy-Stochastic Programming Approach for Municipal Solid Waste Management and Planning

In this study, an integrated fuzzy-stochastic linear programming model is developed and applied to municipal solid waste management. Methods of chance-constrained programming and fuzzy linear programming are incorporated within a general interval-parameter mixed-integer linear programming framework. It improves upon the existing optimization methods with advantages in uncertainty reflection, data availability, and computational requirement. The model can be used for answering questions related to types, times and sites of solid waste management practices, with the objective of minimizing system costs over the planning horizon. The model can effectively reflect dynamic, interactive, and uncertain characteristics of municipal waste management systems. In its solution process, the model is transformed into two deterministic submodels, corresponding to upper and lower bounds of the desired objective function values under a given significance level, based on an interactive algorithm. Results of the method's application to a hypothetical case indicate that reasonable outputs have been obtained. It demonstrates the practical applicability of the proposed methodology.     

A Superiority-Inferiority-Based Inexact Fuzzy Stochastic Programming Approach for Solid Waste Management Under Uncertainty

A superiority–inferiority-based inexact fuzzy stochastic programming (SI-IFSP) model was developed for planning municipal solid waste management systems under uncertainty. The SI-IFSP approach represents a new attempt to tackle multiple uncertainties in objective function coefficients which are beyond the capabilities of existing inexact programming methods. Through introducing the concept of fuzzy random boundary interval, SI-IFSP is capable of reflecting multiple uncertainties (i.e., interval values, fuzzy sets, probability distributions, and their combinations) in both the objective function and constraints, leading to enhanced system robustness. The developed SI-IFSP method was applied to a case study of long-term municipal solid waste management. Useful solutions were generated. A number of decision alternatives could be generated based on projected applicable conditions, reflecting the compromise between system optimality and reliability as well as the tradeoffs between economic and environmental objectives. Moreover, the consequences of system violations could be quantified through introducing a set of economic penalties, reflecting the relationships between system costs and constraint violation risks. The results suggest that the proposed SI-IFSP method can explicitly address complexities in municipal solid waste management systems and is applicable to practical waste management problems.     

Inexact Fuzzy-Stochastic Programming for Water Resources Management Under Multiple Uncertainties

In this study, an interval-parameter fuzzy-stochastic two-stage programming (IFSTP) approach is developed for irrigation planning within an agriculture system under multiple uncertainties. A concept of the distribution with fuzzy-interval probability (DFIP) is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets, and probability distributions. IFSTP integrates the interval programming, two-stage stochastic programming, and fuzzy-stochastic programming within a general optimization framework. IFSTP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. IFSTP is applied to an irrigation planning in a water resources management system. Solutions from IFSTP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions are generated for objective function values and decision variables; thus, a number of decision alternatives can be generated under different levels of stream flows.     

IFTCIP: An Integrated Optimization Model for Environmental Management under Uncertainty

In this study, an interval-fuzzy two-stage chance-constrained integer programming (IFTCIP) method is developed for supporting environmental management under uncertainty. The IFTCIP improves upon the existing interval, fuzzy, and two-stage programming approaches by allowing uncertainties expressed as probability distributions, fuzzy sets, and discrete intervals to be directly incorporated within a general mixed integer linear programming framework. It has advantages in uncertainty reflection, policy investigation, risk assessment, and capacity-expansion analysis in comparison to the other optimization methods. Moreover, it can help examine the risk of violating system constraints and the associated consequences. The developed method is applied to the planning for facility expansion and waste-flow allocation within a municipal solid waste management system. Violations of capacity constraints are allowed under a range of significance levels, which reflects tradeoffs between the system cost and the constraint-violation risk. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different risk levels. They are useful for generating desired decision alternatives with minimized system cost and constraint-violation risk under various environmental, economic, and system-reliability conditions. Generally, willingness to take a higher risk of constraint violation will guarantee a lower system cost; a strong desire to acquire a lower risk will run into a higher system cost.     

Interval-Parameter Conditional Value-at-Risk Two-Stage Stochastic Programming Model for Management of End-of-Life Vehicles

The management of end-of-life vehicles conserves natural resources, provides economic benefits, and reduces water, air, and soil pollution. Sound management of end-of-life vehicles is vitally important worldwide thus requiring sophisticated decision-making tools for optimizing its efficiency and reducing system risk. This paper proposes an interval-parameter conditional value-at-risk two-stage stochastic programming model for management of end-of-life vehicles. A case study is conducted in order to demonstrate the usefulness of the developed model. The model is able to provide the trade-offs between the expected profit and system risk. It can effectively control risk at extremely disadvantageous availability levels of end-of-life vehicles. The formulated model can produce optimal solutions under predetermined decision-making risk preferences and confidence levels. It can simultaneously determine the optimal long-term allocation targets of end-of-life vehicles and reusable parts as well as capital investment, production planning, and logistics management decisions within a multi-period planning horizon. The proposed model can efficiently handle uncertainties expressed as interval values and probability distributions. It is able to provide valuable insights into the effects of uncertainties. Compared to the available models, the resulting solutions are far more robust.

     

DMSP-IEES: A Stochastic Programming Model Based on Dual-Interval and Multi-Stage Scenarios Modeling Approaches for Energy Systems Management and GHG Emissions Control

Energy-related activities contribute a major portion of anthropogenic greenhouse gas (GHG) emissions into the atmosphere. In this study, a dual-interval multi-stage stochastic programming model for the planning of integrated energy-environment systems (DMSP-IEES) model is developed for integrated energy-environment systems management, in which issues of GHG-emission mitigation can be reflected throughout the process of energy systems planning. By integrating methodologies of interval linear programming (when numbers are described as interval values without distribution information), dual-interval programming (when lower and upper bounds of interval values are not available as deterministic values but as discrete intervals), and multi-stage stochastic programming, the DMSP-IEES model is capable of dealing with uncertainties expressed as discrete intervals, dual intervals, and probability distributions within a multi-stage context. Decision alternatives can also be generated through analysis of the single- and dual-interval solutions according to projected applicable conditions. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that the developed model can tackle the dual uncertainties and the dynamic complexities in the energy-environment management systems through a multi-layer scenario tree. In addition, it can reflect the interactions among multiple system components and the associated trade-offs.     

An Inexact Fuzzy-robust Two-stage Programming Model for Managing Sulfur Dioxide Abatement Under Uncertainty

In this study, an inexact fuzzy-robust two-stage programming (IFRTSP) method is developed for tackling multiple forms of uncertainties that can be expressed as discrete intervals, probabilistic distributions and/or fuzzy membership functions. The model can reflect economic penalties of corrective measures against any infeasibilities arising due to a particular realization of system uncertainties. Moreover, the fuzzy decision space can be delimited into a more robust one with the uncertainties being specified through dimensional enlargement of the original fuzzy constraints. A management problem in terms of regional air pollution control has been studied to illustrate the applicability of the proposed approach. Results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers identify desired pollution-abatement strategy with minimized system cost and maximized environmental efficiency.     

Long-Term Planning of Waste Management System in the City of Regina – an Integrated Inexact Optimization Approach

In this study, an integrated solid waste management system based on inexact fuzzy-stochastic mixed integer linear programming (IFSMILP) has been applied to the long-term planning of waste management activities in the City of Regina. The model can effectively reflect dynamic, interactive, and uncertain characteristics of the solid waste management system in the city. The results have provided useful answers for the following questions: “What waste reduction goals are desired if the existing landfill's life is prolonged for 15 years?”, “What should be the waste flow allocation pattern in the city?”, “What should be done if the waste generation rate increases rapidly, while the relevant handling capacity is limited?”, and “What level of reliability will we have given the suggested waste management plan?”     

Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study

In this paper, a new methodology is developed for integrated allocation of water and waste-loads in river basins utilizing a fuzzy transformation method (FTM). The fuzzy transformation method is used to incorporate the existing uncertainties in model inputs. In the proposed methodology, the FTM, as a simulation model, is utilized in an optimization framework for constructing a fuzzy water and waste-loads allocation model. In addition, economic as well as environmental impacts of water allocation to different water users are considered. For equitable water and waste load allocation, all possible coalition of water users are considered and total benefit of each coalition, which is a fuzzy number, is reallocated to water users who are participating in the coalition. The fuzzy cost savings are reallocated using a fuzzy nucleolus cooperative game and the FTM. As a case study, the Dez River system in south-west of Iran is modeled and analyzed using the methodology developed here. The results show the effectiveness of the methodology in optimal water and waste-loads allocations under uncertainty.     

Waste management modeling by MARKAL model: A case study for Basilicata Region

Air pollution is one of the most pressing environmental problems which affects likewise urban, industrial and rural areas. Environmental planners, regulators and decision makers need reliable, scientifically based tools to find out strategies for controlling air pollution in a cost-effective way, taking into account the whole productive system. In this framework the basic elements of energy-environmental planning have to be extended to include also waste processing technologies amongst the usually considered pollution sources. Bottom-up optimizing models, based on linear programming techniques and customized for specific cases, represent a powerful tool in energy-environmental management. This paper focuses on the integrated modeling of material flows and energy system performed on a local scale case study (Basilicata Region, Southern Italy) using the linear programming model IEA-MARKAL. We have evaluated the feasibility of the model in representing the waste management system to estimate the environmental impact of the waste processing technologies in the context of the whole productive system. A sensitivity analysis has been carried out to emphasize the connections between tariffs, waste disposal technologies assessment and atmospheric emissions. This revised version was published online in July 2006 with corrections to the Cover Date.     

A fuzzy multi-stakeholder multi-criteria methodology for water allocation and reuse in metropolitan areas

In this paper, a fuzzy decision making methodology is proposed to find a socially optimal scenario for allocating effluent of wastewater treatment plants and urban and suburban runoffs to agricultural regions and recharging aquifers. The presented methodology named modified fuzzy social choice (MFSC) considers multi-stakeholder multi-criteria problems under uncertainties inherent in a decision making process utilizing a fuzzy ranking method and the fuzzy social choice (FSC) theory. A set of water and wastewater allocation scenarios are proposed for water quantity and quality management of the study area, while six main stakeholders with conflicting utilities and different negotiation powers are involved. The proposed methodology is applied to Tehran metropolitan area, the capital city of Iran with the population of about 8 million people, to examine its applicability and effectiveness. The results shows that using fuzzy multi-stakeholder multi-criteria decision making method considering equal and different negotiation powers can lead to different outcomes. Based on the results, the MFSC method, which considers a number of decision makers having different negotiation powers, degrees of importance of decision making criteria, and some important uncertainties, performs more promising in real water resources management problems.     

Optimal Expansion Strategy for a Sewer System under Uncertainty

Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sewer infrastructure systems. The dominant approaches have turned to planning the sanitary sewer systems within a regional context, while the decentralized and on-site/cluster wastewater systems have not overcome the application barriers. But regionalization policy confers uncertainties and risks upon cities while planning for future events. Following the philosophy of smart growth, this paper presents several optimal expansion schemes for a fast-growing city in the US/Mexico borderlands—the city of Pharr in Texas under uncertainty. The waste stream generated in Pharr is divided into three distinct sewer sheds within the city limit, including south region, central region, and north region. The options available include routing the wastewater to a neighboring municipality (i.e., McAllen) for treatment and reuse, expanding the existing wastewater treatment plant (WWTP) in the south sewer shed, and constructing a new WWTP in the north sewer shed. Traditional deterministic least-cost optimization applied in the first stage can provide a cost-effective and technology-based decision without respect to associated uncertainties system wide. As the model is primarily driven by the fees charged for wastewater transfer, sensitivity analysis was emphasized by the inclusion of varying flat-rate fees for adjustable transfer schemes before contracting process that may support the assessment of fiscal benefits to all parties involved. Yet uncertainties might arise from wastewater generation, wastewater reuse, and cost increase in constructing and operating the new wastewater treatment plant simultaneously. When dealing with multiple sources of uncertainty, the grey mixed integer programming (GIP) model, formulated in the second stage, can further allow all sources of uncertainties to propagate throughout the optimization context, simultaneously leading to determine a wealth of optimal decisions within a reasonable range. Both models ran for three 5-year periods beginning in 2005 and ending in 2020. The dynamic outputs of this analysis reflect the systematic concerns about integrative uncertainties within this decision analysis, which enable decision makers and stakeholders to make all-inclusive decisions for sanitary sewer system expansion in an economically growing region.     

Optimization and Decision Heuristics for Chesapeake Bay Nutrient Reduction Strategies

Regional policies to achieve water quality goals assign a unique pollution control technology to every pollution source in a watershed, thereby defining a watershed strategy. For watersheds with even a modest number of pollution sources and control alternatives, the decision problem has combinatorial complexity. The perception of complexity—manifested in innumerable feasible watershed strategies—commonly induces the use of simplifying decision heuristics and ad hoc decision rules that reduce decision complexity by limiting the choice set to a “manageable” number of alternatives. In problems with large complex choice sets, these decision heuristics simplify decision making by excluding the vast majority of feasible alternatives a priori. In contrast, watershed-scale optimization enables decision makers to consider all feasible alternatives implicitly, exploiting rather than restricting the complexity of the feasible choice set. This contrast is illustrated using mixed-integer linear programming to identify interstate watershed strategies that achieve Chesapeake Bay nutrient reduction goals for the Potomac River Basin. The use of optimization in collaborative decision making helped refine and capture decision makers’ underlying values and preferences in policy-relevant constraints reflecting equity and political feasibility. Optimization formulations incorporating these constraints identified more effective and desirable management alternatives that would not otherwise have been considered using familiar decision heuristics and traditional comparisons among a limited number of ad hoc scenarios. Incorporating optimization in collaborative decision making generated superior watershed strategies and eased the cognitive limitations on decision making by substituting the computational burden of solving mixed-integer linear programs for decision makers’ cognitive burden of enumerating alternatives and scenarios for environmental systems with combinatorial complexity.     

A Routing and Scheduling System for Infectious Waste Collection

Most hospitals and clinics in Taiwan do not have on-site treatment facilities for their infectious waste and must rely on outside agencies for their collection and treatment. The problem of optimally routing and scheduling the collection of medical waste from a disperse group of facilities is recognized as a periodic vehicle routing problem. This study develops a computer system to solve the resulting optimization problem based on a two-phased approach proposed earlier. The first phase solves a standard vehicle routing problem to determine a set of individual routes for the collection vehicles. The second phase uses a mixed integer programming method to assign routes to particular days of the week. The computer system is user-friendly and consists of several Visual Basic programs while the geographical information system is incorporated to facilitate input and output interface and database management. An illustrative example for the infectious waste of 348 hospitals in the Tainan City area demonstrates the effectiveness of the system.     

A Linear-Programming-Based Strategy for Bioremediation of Oil-Polluted Marine Environments

A linear programming problem is considered with the aim to determine the optimal discharge point and the optimal discharge rate of a nutrient to be released to a marine environment polluted with oil. The objective is to minimize the total discharge of nutrient into the system provided that the concentrations of nutrient will reach critical values sufficient to eliminate oil residuals in certain affected zones through bioremediation. An initial boundary-value 3D problem for the advection–diffusion equation and its adjoint problems are considered to model, estimate, and control the dispersion of nutrient in a limited region. It is shown that the advection–diffusion problem is well posed, and its solution satisfies the mass balance equation. In each oil-polluted zone, the mean concentration of nutrient is determined by means of an integral formula in which the adjoint model solution serves as a weight function. Critical values of these mean concentrations are used as the constraints of linear programming problem. Some additional constraints are posed in order to limit not only the local discharge of the nutrient, but also the mean concentration of this substance in the whole region. Both constraints serve for environmental protection. The ability of the new method is demonstrated by numerical experiments on the remediation in oil-polluted channel using three control zones. The experiments show that the optimal discharge rate can always be got with a simple combination of step functions.     

国家重点生态功能区县域环境监测质量评价方法及应用示范

以国家重点生态功能区县域环境监测质量评价为目标,综合应用德尔菲法、层次分析法和模糊综合评价法,构建了国家重点生态功能区县域环境监测质量评价方法,并确定了评价因子、权重系数、计算方法。该方法评价指标共分为三层:第一层为目标层,即国家重点生态功能区县域环境监测质量;第二层为准则层,包括人员及资质、现场监测、实验室管理、报告编制及数据上报;第三层为方案层,包括人员操作、持证上岗、资质认定、人员培训、水质布点采样流转情况、空气自动站运维情况、现场质控实施情况、实验室环境条件、样品试剂的保存与管理、仪器检定与校准、实验室质量控制实施情况、数据填报软件运行情况、监测报告规范性等13个评价要素。经矩阵一致性检验确定了各评价要素的权重,将该权重与各要素得分运算后得到县域环境监测质量评价结果。在此基础上,选取广东、山西、陕西、四川和青海等5个省份的15个国家重点生态功能区县域作为典型区开展了实地调研,并应用评价体系对其进行了监测质量等级评价。结果表明,15个典型县域中,环境监测质量等级为优的县域占13.3%,一般、较差的县域分别占66.7%、20%。县域环境监测承担单位在资质、报告编制及数据上报方面表现较好,在现场监测、人员操作方面问题突出,在实验室管理方面有待提升。     

Water and waste load allocation in rivers with emphasis on agricultural return flows: application of fractional factorial analysis

In this paper, a new methodology is developed to handle parameter and input uncertainties in water and waste load allocation (WWLA) in rivers by using factorial interval optimization and the Soil, Water, Atmosphere, and Plant (SWAP) simulation model. A fractional factorial analysis is utilized to provide detailed effects of uncertain parameters and their interaction on the optimization model outputs. The number of required optimizations in a fractional factorial analysis can be much less than a complete sensitivity analysis. The most important uncertain inputs and parameters can be also selected using a fractional factorial analysis. The uncertainty of the selected inputs and parameters should be incorporated real time water and waste load allocation. The proposed methodology utilizes the SWAP simulation model to estimate the quantity and quality of each agricultural return flow based on the allocated water quantity and quality. In order to control the pollution loads of agricultural dischargers, it is assumed that a part of their return flows can be diverted to evaporation ponds. Results of applying the methodology to the Dez River system in the southwestern part of Iran show its effectiveness and applicability for simultaneous water and waste load allocation in rivers. It is shown that in our case study, the number of required optimizations in the fractional factorial analysis can be reduced from 64 to 16. Analysis of the interactive effects of uncertainties indicates that in a low flow condition, the upstream water quality would have a significant effect on the total benefit of the system.     

Utility of fuzzy cross-impact simulation in environmental assessment

Fuzzy cross-impact simulation is a qualitative technique, where interactions within a system are represented by a cross-impact matrix that includes linguistic elements. It can be used effectively to visualize dynamic evolution of a system. The utility of the fuzzy cross-impact simulation approach is: (1) in dealing with uncertainties in environment-development systems; (2) scoping cumulative effect assessment; and (3) integrating societal response structure in environment impact assessment. Use of the method is illustrated in a case concerning the textile industry in Indore, India. Consequences of policy alternatives for cleanup and pollution abatement are predicted in terms of environmental quality and quality of life, using the simulation model. The consequence analysis is used to arrive at preferred policy options.     

固体废物氰化物总量和浸出毒性测定方法的比较

中国现行的固体废物氰化物总量和氰化物浸出毒性的分析方法存在缺陷,不便于广泛指导监测工作,笔者优化了固体废物氰化物测定的前处理方法,明确了固体废物氰化物总量、氰化物浸出毒性测定时的样品粒径、浸提方法和消解方法,建立了容量法、分光光度法、流动注射法测定固体废物氰化物总量和浸出毒性的方法,并与标准方法（离子色谱法）进行比较。实验结果表明：容量法、分光光度法、流动注射法测定结果与离子色谱法无显著差异,3种方法测定固体废物氰化物总量加标回收率为80.5%~102%,平行样测定相对标准偏差为3.0%~6.9%,3种方法测定固体废物氰化物浸出毒性加标回收率为80.1%~107%,平行样测定相对标准偏差为7.8%~9.5%,3种方法测定结果精密度和准确度良好,均能够满足固体废物氰化物总量和氰化物浸出毒性的测定要求。其中容量法、分光光度法由于其仪器设备简单、操作简便,可用于突发环境事件应急监测等情况下固体废物氰化物的测定。但容量法检出限较高,不能满足评价标准较低的分析测试工作要求,离子色谱法、分光光度法和流动注射法检出限均能满足一般分析测试要求。     

基于健康风险的危险废物智能化分级分类研究

在分析国内外危险废物分级管理经验的基础上,结合健康风险的评估步骤,提出了基于全过程危险废物污染物释放情景的精细化-动态健康风险评价方法。充分考虑处理利用工艺、企业管理水平等因素对于危险废物中污染物释放概率和能力的影响,并基于污染物向环境介质的迁移转化,定量评估危险废物的健康风险。基于危险废物污染特性数据库和事故情景数据库,结合全过程信息采集技术,构建危险废物分级分类管理平台,进行涉废企业的风险级别划分,实施差异化管理,形成更加科学有效的危险废物全过程精细化管理体系。