Y.L. Xie

An inexact chance-constrained programming model for water quality management in Binhai New Area of Tianjin, China

In this study, an inexact-chance-constrained water quality management (ICC-WQM) model is developed for planning regional environmental management under uncertainty. This method is based on an integration of interval linear programming (ILP) and chance-constrained programming (CCP) techniques. ICC-WQM allows uncertainties presented as both probability distributions and interval values to be incorporated within a general optimization framework. Complexities in environmental management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method is applied to planning chemical-industry development in Binhai New Area of Tianjin, China. Interval solutions associated with different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help decision makers identify desired policies under various system-reliability constraints of water environmental capacity of pollutant. Tradeoffs between system benefits and constraint-violation risks can also be tackled. They are helpful for supporting (a) decision of wastewater discharge and government investment, (b) formulation of local policies regarding water consumption, economic development and industry structure, and (c) analysis of interactions among economic benefits, system reliability and pollutant discharges.

An inexact two-stage stochastic programming model for water resources management in Nansihu Lake Basin, China

In this study, an inexact two-stage water resources management model was developed for multi-regional water resources planning in the Nansihu lake Basin, China. Four planning districts, four water users, and five water sources were considered in the optimization model, with net system benefit, recourse cost, water supply cost, and wastewater treatment cost being analyzed. Methods of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) were incorporated into the model to tackle uncertainties described by both interval values and probability distributions. A number of scenarios corresponding to different river inflow levels were examined, and the results indicated that different inflow levels could lead to different water allocation schemes with varied system benefit and system-failure risk. In general, the developed model can provide an effective linkage between economic benefits and the associated penalties attributed to the violation of predefined policies. The modeling results were valuable for supporting the adjustment or justification of the existing water allocation schemes within a complicated water resources system under uncertainty.

An interval-based regret-analysis method for identifying long-term municipal solid waste management policy under uncertainty.

In this study, an interval-based regret-analysis (IBRA) model is developed for supporting long-term planning of municipal solid waste (MSW) management activities in the City of Changchun, the capital of Jilin Province, China. The developed IBRA model incorporates approaches of interval-parameter programming (IPP) and minimax-regret (MMR) analysis within an integer programming framework, such that uncertainties expressed as both interval values and random variables can be reflected. The IBRA can account for economic consequences under all possible scenarios associated with different system costs and risk levels without making assumptions on probabilistic distributions for random variables. Axa0regret matrix with interval elements is generated based on a matrix of interval system costs, such that desired decision alternatives can be identified according to the interval minimax regret (IMMR) criterion. The results indicate that reasonable solutions have been generated. They can help decision makers identify the desired alternatives regarding long-term MSW management with a compromise between minimized system cost and minimized system-failure risk.

An inexact mixed risk-aversion two-stage stochastic programming model for water resources management under uncertainty

Uncertainties exist in the water resources system, while traditional two-stage stochastic programming is risk-neutral and compares the random variables (e.g., total benefit) to identify the best decisions. To deal with the risk issues, a risk-aversion inexact two-stage stochastic programming model is developed for water resources management under uncertainty. The model was a hybrid methodology of interval-parameter programming, conditional value-at-risk measure, and a general two-stage stochastic programming framework. The method extends on the traditional two-stage stochastic programming method by enabling uncertainties presented as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. It could not only provide information on the benefits of the allocation plan to the decision makers but also measure the extreme expected loss on the second-stage penalty cost. The developed model was applied to a hypothetical case of water resources management. Results showed that that could help managers generate feasible and balanced risk-aversion allocation plans, and analyze the trade-offs between system stability and economy.

Development of an inexact two-stage stochastic model with downside risk control for water quality management and decision analysis under uncertainty

Water quality management along rivers involves making water-allocation plans, establishing water quality goals, and controlling pollutant discharges, which is complicated itself but further challenged by existence of uncertainties. In this study, an inexact two-stage stochastic downside risk-aversion programming (ITSDP) model is developed for supporting regional water resources allocation and water quality management problems under uncertainties. The ITSDP method is a hybrid of interval-parameter programming, two-stage stochastic programming, and downside risk measure to tackle uncertainties described in terms of interval values and probability distributions. A water quality simulation model was provided for reflecting the relationship between the water resources allocation, wastewater discharge, and environmental responses. The proposed approach was applied to a hypothetical case for a shared stream water quality management with one municipal, three industrial and two agricultural sectors. A number of scenarios corresponding to different river inflows and risk levels were examined. The results demonstrated that the model could effectively communicate the interval-format and random uncertainties, and risk-aversion into optimization process, and generate a trade-off between the system economy and stability. They could be helpful for seeking cost-effective management strategies under uncertainties, and gaining an in-depth insight into the water quality management system characteristics, and make cost-effective decisions.

A multistage stochastic robust optimization model with fuzzy probability distribution for water supply management under uncertainty

In this study, a fuzzy probability distribution- based multi-stage stochastic robust programming method has been developed for supporting regional water supply management. In the proposed model, methods of interval-parameter programming and robust stochastic optimization, and fuzzy probability distribution are introduced into a multi-stage stochastic programming framework, and the developed model can tackle uncertainties described in terms of interval values and fuzzy probability distributions. The developed model was applied to a water resources management system with three water users. A number of scenarios corresponding to different river inflow and α-cut levels are examined; the results suggest that reasonable solutions have been generated for regional water resources management. The results indicated that the optimization model’s outputs were highly dependent on the complex uncertain features of the study system, and the α-cut level of fuzzy probability had few significant impacts on the system objective. The results also implied that the developed method can be used for analyzing a variety of policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. Dynamics and uncertainties of water availability (and thus water allocation and shortage) could be taken into account through generation of a set of representative scenarios within a multistage context. The proposed method could be used by environmental managers to evaluate trade-offs of system benefits and risk involving fuzzy probability condition, as well as identify management solutions that sufficiently hedge against dual uncertainties.

A Risk-Based Balance Inexact Optimization Model for Water Quality Management with Sustainable Wetland System Development—A Case Study of North China

In this study, a risk-based balance inexact water resources optimization model for considering wetland ecological water demand and water quality problems, based on interval-parameter programming, fuzzy two-stage stochastic programming, and downside risk-aversion measure, is developed for regional water resources management in Nansihu Lake basin, Shangdong province, China. The developed model can tackle uncertainties described in terms of interval values and probability distributions. Moreover, risk aversion is incorporated by limiting the volatility of the expected profit through downside risk methodology, in order to limit the risk of failing to reach an income target of competitive regions in the lake basin and reflect the preference of decision makers, such that the tradeoff between system economy, ecological water demand, and income target could be analyzed. All suggested scenarios (e.g. the plausibility degree of ecological water demand and the risk level of unbalance income) are determined by management requirement. The results indicated that different water inflow and ecological-related water demand levels correspond to different water shortages and allocation schemes of different water sources, and thus lead to varied system benefit and system-failure risk. The proposed model is valuable for supporting mid-/long-term water resources management under economic, environmental, ecological, and system balance development considerations.

A Novel Inexact Two-Stage Stochastic Robust-Compensation Model for Electric Supply Environmental Management Under Uncertainty

In this study, a novel inexact two-stage stochastic robust-compensation programming (ITSP-RC) model is developed for CO2 emission reduction management under uncertainties. This model is attempted to integrate ITSP and stochastic RC programming into a general framework and apply the ITSP-RC for power management and CO2 emission reduction management, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a two-stage context. Moreover, it can reflect dynamic and randomness of the energy systems during the planning horizon. The developed method has been applied to a case to solve CO2 emission management problem in electric supply environmental management. A number of scenarios corresponding to different adoption rate levels of carbon capture, utilization, and storage technology are examined. With the RC programming, regional energy systems would have a stable financial budget. The result suggests that the methodology is applicable for reflecting complexities of large-scale energy management systems and addressing CO2 emissions reduction issue with the planning period.

An optimization model for regional air pollutants mitigation based on the economic structure adjustment and multiple measures: A case study in Urumqi city, China.

A model based on economic structure adjustment and pollutants mitigation was proposed and applied in Urumqi. Best-worst case analysis and scenarios analysis were performed in the model to guarantee the parameters accuracy, and to analyze the effect of changes of emission reduction styles. Results indicated that pollutant-mitigations of electric power industry, iron and steel industry, and traffic relied mainly on technological transformation measures, engineering transformation measures and structure emission reduction measures, respectively; Pollutant-mitigations of cement industry relied mainly on structure emission reduction measures and technological transformation measures; Pollutant-mitigations of thermal industry relied mainly on the four mitigation measures. They also indicated that structure emission reduction was a better measure for pollutants mitigation of Urumqi. Iron and steel industry contributed greatly in SO2, NOx and PM (particulate matters) emission reduction and should be given special attention in pollutants emission reduction. In addition, the scales of iron and steel industry should be reduced with the decrease of SO2 mitigation amounts. The scales of traffic and electric power industry should be reduced with the decrease of NOx mitigation amounts, and the scales of cement industry and iron and steel industry should be reduced with the decrease of PM mitigation amounts. The study can provide references of pollutants mitigation schemes to decision-makers for regional economic and environmental development in the 12th Five-Year Plan on National Economic and Social Development of Urumqi.

An inexact fuzzy bi-level programming model for energy–traffic system planning under uncertainty: a case study of Urumqi city, China

ABSTRACT In this study, an inexact fuzzy bi-level programming model was developed for regional energy and traffic system management under uncertainty in Urumqi city, China. The energy system and traffic system are important subsystems of regional areas such as cities. The coordinated management of regional subsystems is a difficult problem for regional management. A bi-level programming model is an appropriate and simple method to describe the coordinated management of regional subsystems. The energy and traffic structure adjustment, clean power generation and pollutant emission–reduction targets are designed to support the construction of an environmentally sustainable city in China. Methods of interval parameter programming and bi-level programming were incorporated into the developed model to tackle uncertainties and reflect the features in the system. The environmental impacts of energy and traffic activities and policies were analysed. The results are valuable for supporting the management or justification of the existing energy and traffic policies and schemes under uncertainty.