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.     

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.     

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.     

Interval-parameter Fuzzy-stochastic Semi-infinite Mixed-integer Linear Programming for Waste Management under Uncertainty

An interval-parameter fuzzy-stochastic semi-infinite mixed-integer linear programming (IFSSIP) method is developed for waste management under uncertainties. The IFSSIP method integrates the fuzzy programming, chance-constrained programming, integer programming and interval semi-infinite programming within a general optimization framework. The model is applied to a waste management system with three disposal facilities, three municipalities, and three periods. Compared with the previous methods, IFSSIP have two major advantages. One is that it can help generate solutions for the stable ranges of the decision variables and objective function value under fuzzy satisfaction degree and different levels of probability of violating constraints, which are informative and flexible for solution users to interpret/justify. The other is that IFSSIP can not only handle uncertainties through constructing fuzzy and random parameter, but also reflect dynamic features of the system conditions through interval function of time over the planning horizon. By comparing IFSSIP with interval-parameter mixed-integer linear semi-infinite programming and parametric programming, the IFSSIP method is more reasonable than others.     

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.     

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.

     

A Satisficing Framework for Environmental Policy Under Model Uncertainty

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

     

Decision Analysis Methodology to Evaluate Integrated Solid Waste Management Alternatives

Value-focused thinking, a decision analysis technique, is used to produce a multiple-objective model that captures a site's municipal solid waste (MSW) management goals, objectives, and concerns in order to facilitate the evaluation of competing strategies. The model ranks competing MSW alternatives based on how well they meet the decision maker's strategic objective, a 20-year compliant MSW system. Sensitivity analysis is incorporated in the model to assess and illustrate the effects of changes in model objective weights and changes in model parameters. Overall, the model provides decision-makers with a decision tool to make a better decision when choosing a new MSW management strategy. The entire process is applied to a case study using Earekson Air Station, a remote U.S. Air Force installation.     

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.     

A Time-Dependent System for Evaluating Groundwater Contamination Hazard Rating of Municipal Solid Waste Dumps

In developing countries, several old municipal solid waste dumps (unlined landfills) exist adjacent to large cities, releasing contaminants to the underlying aquifer, thus posing the hazard of groundwater contamination. These uncontrolled waste dumps need to be prioritized in terms of the groundwater contamination hazard posed by them, so that necessary control and remedial measures can be undertaken in a phased manner. This paper presents a time-dependent system for evaluating groundwater contamination hazard rating of municipal solid waste dumps. The system is based on source–pathway–receptor relationships and evaluates the relative value of hazard posed by a site over its entire leaching life, on a scale of 0–1,000. The system parameters have been selected based on literature and expert opinions. The Delphi technique is used to derive the relative importance weights of the system parameters. The proposed system is compared with six selected existing hazard rating systems. The comparison, made by way of score range analysis, shows that the proposed system exhibits a much wider range of hazard scores for various scenarios of site conditions, and hence the proposed system is more sensitive to varied site conditions. The application of different systems to six municipal solid waste dumps located in four cities of India shows that, whereas the existing systems individually produce clustered scores and return the same rank to more than one site, the proposed system produces significantly varying scores and return different ranks to different sites. This demonstrates that the proposed system improves decision making and makes a better basis for prioritization of municipal solid waste dumps for adopting control and remedial measures.     

A Decision Support System Approach for Rivers Monitoring and Sustainable Management

This paper presents a Decision Support System (DSS) approach developed in the context of the Copernicus project entitled System for Water Monitoring and Sustainable Management based on Ground Stations and Satellite Images (WATERMAN). The main objective of WATERMAN is the monitoring and management of the Strymon River in the Southern Balkans. The specific DSS integrates the main components of WATERMAN and helps the decision maker to monitor the Strymon region; to control and forecast the quantity and quality of the river water; as well as to make objective decisions about the state of the water based on data provided by radio computers, earth stations and satellite images processed by mathematical and statistical models and Geographical Information Systems (GIS).     

A Fuzzy Logic Approach to Assess Groundwater Pollution Levels Below Agricultural Fields

A fuzzy logic approach has been developed to assess the groundwater pollution levels below agricultural fields. The data collected for Kumluca Plain of Turkey have been utilized to develop the approach. The plain is known with its intensive agricultural activities, which imply excessive application of fertilizers. The characteristics of the soils and underlying groundwater for this plain were monitored during the years 1999 and 2000. Additionally, an extensive field survey related to the types and yields of crops, fertilizer application and irrigation water was carried out. Both the soil and groundwater have exhibited high levels of nitrogen, phosphorus and salinity with considerable spatial and temporal variations. The pollution level of groundwater at several established stations within the plain were assessed using Fuzzy Logic. Water Pollution Index (WPI) values are calculated by Fuzzy Logic utilizing the most significant groundwater pollutants in the area namely nitrite, nitrate and orthophosphate together with the groundwater vulnerability to pollution. The results of the calculated WPI and the monitoring study have yielded good agreement. WPI indicated high to moderate water pollution levels at Kumluca plain depending on factors such as agricultural age, depth to groundwater, soil characteristics and vulnerability of groundwater to pollution. Fuzzy Logic approach has shown to be a practical, simple and useful tool to assess groundwater pollution levels.     

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.     

Developing a Decision-Support System for Waste Management in Aluminum Production

Industrial enterprises constitute a major portion of the world’s economy, as well as a large proportion of a country’s businesses and total employment. In Turkey, industrial enterprises are underdeveloped in terms of knowledge, skill, capital, and particularly accessing and benefiting from the advantages provided by modern information and communication technologies. Aluminum manufacturing has been reported to be the largest industry in Turkey with respect to production volumes and application fields. However, aluminum production is known to be an important contributor to environmental pollution, and the relative contribution of other related enterprises to the total industrial environmental impact is unknown. Environmental pollution sources can typically be classified into three categories: gaseous emissions, solid wastes, and wastewaters. The types of wastes produced by aluminum production vary based on the process line used, the variety of target products produced, and the production capacity of a given plant. As the capacities of facilities grow, the type and amount of waste become more variable. Therefore, the primary objective of this study is to determine the priority of each waste type in aluminum manufacturing industries. This study was conducted in the Industrial Zone of Kayseri in Turkey. Three different facilities that range in size from large to small based on their production volume, plant capacity, and variety of production are selected for this study. The priority of waste types was determined by combining the AHP and PROMETHEE II multicriteria decision methods. While wastewater was categorized as having the highest priority in large facilities, solid waste was determined to be the highest priority in medium and small facilities.     

Why Use One Model? An Approach for Encompassing Model Uncertainty and Improving Best Practice

There has been some discussion within the UK modelling community regarding the preferred choice of dispersion model for assessing the air quality impact of elevated point sources, each of which is reported to perform better than the other in certain circumstances. Coupled to this is the growing acceptance within the modelling community to include some recognition of model uncertainty in its reporting. As computer processing speeds increase further, this paper considers the possibility of using more than one model and highlights some of the advantages in doing so.     

A Jackknife Approach to Examine Uncertainty and Temporal Change in the Spatial Correlation of a VOC Plume

The application of geostatistics to spatial interpolation of time-invariant properties in ground-water studies (such as transmissivity or aquifer thickness) is well documented. The use of geostatistics on time-variant conditions such as ground-water quality is also becoming more commonplace. Unfortunately, the detection of temporal changes in spatial correlation through direct comparison of experimental semivariograms is difficult due to the uncertainty in sample semivariograms constructed from field data. This paper discusses the use of the jackknife approach to estimate confidence limits of semivariograms of trichloroethane (TC) and other volatile organic compounds (VOC) in contaminated ground-water in northern Illinois. Examination of the spread of the confidence limits about the semivariograms created from two types of sampling networks are discussed.     

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 Model for Landscape Planning Under Complex Spatial Conditions

We present a new mathematical programming framework that is adaptable to a variety of spatially explicit landscape problems in environmental investment, conservation, and land-use planning, transport planning, and agriculture. As part of capturing spatial interdependencies, the framework considers decision variables at two levels, finely spaced grid cells and landholdings. We applied the framework to an environmental investment problem using objective functions representing biodiversity and carbon sequestration. We also tested the model to optimize the path of a road through part of the landscape. Using the Nambucca case study in eastern Australia, we applied a hybrid greedy randomised adaptive search procedure (GRASP) to find solutions to the model.