An inexact-stochastic with recourse model for developing regional economic-ecological sustainability under uncertainty

被引:17
|
作者
Li, Y. P. [1 ]
Huang, G. H. [2 ]
Zhang, N. [3 ]
Nie, S. L. [4 ]
机构
[1] Peking Univ, Coll Urban & Environm Sci, Lab Earth Surface Proc, Beijing 100871, Peoples R China
[2] Univ Regina, Fac Engn, Environm Syst Engn Program, Regina, SK S4S 0A2, Canada
[3] China Int Ctr Econ & Tech Exchanges, Beijing 100007, Peoples R China
[4] Beijing Univ Technol, Coll Mech Engn & Appl Elect Technol, Beijing 100022, Peoples R China
关键词
Decision making; Ecological; Modeling; Optimization; Stochastic with recourse; Sustainability; Uncertainty; Resources management; WATER-RESOURCES MANAGEMENT; QUALITY MANAGEMENT; PROGRAMMING MODEL; DECISION-MAKING; RIVER; OPTIMIZATION; SYSTEM;
D O I
10.1016/j.ecolmodel.2009.12.010
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Effective planning of resources management is important for facilitating socio-economic development and eco-environmental sustainability. Such a planning effort is complicated with a variety of uncertain, dynamic and nonlinear factors as well as their interactions. In this study, an inexact-stochastic quadratic programming with recourse (ISQP-R) method is developed for reflecting dynamics of system uncertainties based on a complete set of scenarios as well as tackling nonlinearities in the objective function to reflect the effects of marginal utility on system benefits and costs. Moreover, since penalties are exercised with recourse against any infeasibility, the ISQP-R can support the analysis of various policy scenarios that are associated with different levels of economic consequences when the promised targets are violated. The developed method is applied to a case study of planning resources management and developing regional ecological sustainability. The results have been generated and are helpful for decision makers in not only identifying desired resources-allocation strategies but also gaining insight into the tradeoff between economic objective and eco-environment violation risk. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:370 / 379
页数:10
相关论文
共 29 条
  • [21] A Genetic-Algorithm-Aided Stochastic Optimization Model for Regional Air Quality Management under Uncertainty
    Qin, Xiaosheng
    Huang, Guohe
    Liu, Lei
    JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2010, 60 (01) : 63 - 71
  • [22] Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty
    Ji, Ling
    Huang, Guo-He
    Huang, Lu-Cheng
    Xie, Yu-Lei
    Niu, Dong-Xiao
    ENERGY, 2016, 109 : 920 - 932
  • [23] An inexact fixed-mix fuzzy-stochastic programming model for heat supply management in wind power heating system under uncertainty
    Wu, C. B.
    Huang, G. H.
    Li, W.
    Zhen, J. L.
    Ji, L.
    JOURNAL OF CLEANER PRODUCTION, 2016, 112 : 1717 - 1728
  • [24] Inexact rough-interval type-2 fuzzy stochastic optimization model supporting municipal solid waste management under uncertainty
    Wang, Li
    Jin, Lei
    ENGINEERING OPTIMIZATION, 2019, 51 (09) : 1567 - 1580
  • [25] Two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty
    Huang, Yun-Hsun
    Wu, Jung-Hua
    Hsu, Yu-Ju
    ENERGY, 2016, 116 : 1145 - 1157
  • [26] Developing a Two-Stage Stochastic Programming Model for CO2 Disposal Planning under Uncertainty
    Han, Jee-Hoon
    Ryu, Jun-Hyung
    Lee, In-Beum
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (08) : 3368 - 3380
  • [27] IFTEM: An interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty
    Lin, Q. G.
    Huang, G. H.
    Bass, B.
    Qin, X. S.
    ENERGY POLICY, 2009, 37 (03) : 868 - 878
  • [28] Development of an interval multi-stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty
    Li, Gongchen
    Huang, Guohe
    Lin, Qianguo
    Cai, Yanpeng
    Chen, Yumin
    Zhang, Xiaodong
    INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2012, 36 (12) : 1161 - 1174
  • [29] A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty
    Bhavsar, S.
    Pitchumani, R.
    Ortega-Vazquez, M. A.
    Costilla-Enriquez, N.
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2023, 151