Multistage scenario-based interval-stochastic programming for planning water resources allocation

In this study, a multistage scenario-based interval-stochastic programming (MSISP) method is developed for water-resources allocation under uncertainty. MSISP improves upon the existing multistage optimization methods with advantages in uncertainty reflection, dynamics facilitation, and risk analysis. It can directly handle uncertainties presented as both interval numbers and probability distributions, and can support the assessment of the reliability of satisfying (or the risk of violating) system constraints within a multistage context. It can also reflect the dynamics of system uncertainties and decision processes under a representative set of scenarios. The developed MSISP method is then applied to a case of water resources management planning within a multi-reservoir system associated with joint probabilities. A range of violation levels for capacity and environment constraints are analyzed under uncertainty. Solutions associated different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help water managers to identify desired policies under various economic, environmental and system-reliability conditions. Besides, sensitivity analyses demonstrate that the violation of the environmental constraint has a significant effect on the system benefit

Integrated Modeling System for Water Resources Management of Tarim River Basin

An integrated modeling system has been developed for water resources management of the Tarim River Basin, China. The system coupled remote sensing (RS)/geography information system (GIS) technique with distributed hydrological model to simulate the rainfall runoff, snow melting, and evapotranspiration process of the hydrological cycle. A case study was carried out in the Kaidu watershed. RS/GIS technique was used for effectively accessing, processing, and managing spatial data, such as land use, vegetative cover, soil, topography, precipitation, and evapotranspiration. The model was calibrated and validated against observed discharge for two hydrological stations during the period 1998-2001, and it generally performed well for Nash-Sutcliffe coefficient, water balance coefficient, and correlation coefficient. The Nash-Sutcliffe coefficient was approximately over 0.7 and the water balance error was lower than +/- 5%, indicating reasonable prediction accuracy. A comparison between the conventional and RS-based hydrological models was conducted. Although the two-models exhibit similar performances on runoff and snow melt simulation, the RS-based hydrological model had better performance in the simulation of actual evapotranspiration. Modeling results provide useful decision support for water resources management

Interval-Parameter Robust Minimax-regret Programming and Its Application to Energy and Environmental Systems Planning

In this study, an interval-parameter robust minimax-regret programming method is developed and applied to the planning of energy and environmental systems. Methods of robust programming, interval-parameter programming, and minimax-regret analysis are incorporated within a general optimization framework to enhance the robustness of the optimization effort. The interval-parameter robust minimax-regret programming can deal with uncertainties expressed as discrete intervals, fuzzy sets, and random variables. It can also be used for analyzing multiple scenarios associated with different system costs and risk levels. In its solution process, the fuzzy decision space is delimited into a more robust one through dimensional enlargement of the original fuzzy constraints; moreover, an interval-element cost matrix can be transformed into an interval-element regret matrix, such that the decision makers can identify desired alternatives based on the inexact minimax regret criterion. The developed method has been applied to a case study of energy and environmental systems planning under uncertainty. The results indicate that reasonable solutions have been generated

Inexact fuzzy-stochastic constraint-softened programming - A case study for waste management

In this study, an inexact fuzzy-stochastic constraint-softened programming method is developed for municipal solid waste (MSW) management under uncertainty, The developed method can deal with multiple uncertainties presented in terms of fuzzy sets, interval values and random variables. Moreover, a number of violation levels for the system constraints are allowed. This is realized through introduction of violation variables to soften system constraints, such that the model's decision space can be expanded under demanding conditions. This can help generate a range of decision alternatives under various conditions, allowing in-depth analyses of tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The developed method is applied to a case study of planning a MSW management system. The uncertain and dynamic information can be incorporated within a multi-layer scenario tree; revised decisions are permitted in each time period based on the realized values of uncertain events. Solutions associated with different satisfaction degree levels have been generated, corresponding to different constraint-violation risks. They are useful for supporting decisions of waste flow allocation and system-capacity expansion within a multistage context. (C) 2008 Elsevier Ltd. All rights reserved

A two-stage inexact-stochastic programming model for planning carbon dioxide emission trading under uncertainty

In this study, a two-stage inexact-stochastic programming (TISP) method is developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed TISP incorporates techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within a general optimization framework. The TISP can not only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated greenhouse gas (GHG) management policies and the associated economic implications. The developed method is applied to a case study of energy systems and CO2 emission trading planning under uncertainty. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired GHG abatement policies under various economic and system-reliability constraints. (C) 2009 Elsevier Ltd. All rights reserved

Bibliometrics analysis on the research status and trends of adult-onset Still’s disease: 1921-2021

ObjectivesThe aim of this research is to discuss the research status, hotspots, frontiers and development trends in the field of adult-onset Still’s disease (AOSD) based on bibliometrics and visual analysis by CiteSpace software.MethodsThe relevant research articles on AOSD from 1921 to 2021 were retrieved from the Scopus database. CiteSpace software was used to form a visual knowledge map and conduct analysis for the countries/regions, journals, authors, keywords, clusters, research hotspots and frontiers of the included articles.ResultsThere were 2,373 articles included, and the number of articles published during 1921-2021 is increasing. The country with the highest number of articles published was Japan (355, 14.96%), followed by the United States (329, 13.86%) and France (215, 9.06%). The author with the highest number of publications is Ansell, Barbara M. (30, 1.26%), and the author with the highest co-citation frequency is Yamaguchi, Masaya (703). Clinical Rheumatology is the journal with the highest publication frequency. The top five cluster groups were “joint”, “differential diagnosis”, “prednisolone”, “methotrexate” and “macrophage activation syndrome”. The diagnosis, treatment and pathogenesis of AOSD form the main research fields, and prognosis and complications are the research hotspots and trends.ConclusionsThe global research field in AOSD has expanded in the past 100 years. The complications and new pathogenesis of AOSD are hotspots in this field and need further study in the future