Inexact stochastic optimization model for industrial water resources allocation under considering pollution charges and revenue-risk control

Abstract In this study, an inexact two-stage stochastic downside risk-aversion programming is developed for regional industrial water resources allocation under considering system return-risk and various environment control strategies. In the model, interval-parameter programming, two-stage stochastic programming, and downside risk measure are introduced into an integrated framework for reflecting the complexity and uncertainty of industrial system, and avoiding the expected revenue risk. The method could not only reflect industrial water resources allocation characteristic among multiple users and suppliers, but also provide an effective linkage between economic cost and the system stability. The model is applied to a real case of industrial water resources allocation management in Chongqing city, China, where regional industrial system has faced with lots of difficulties and complexities in water resources utilization and water environmental protection. The impact of pollutants emission reduction and risk-aversion attitude on water resources allocation for different industry sectors, system benefits, and pollutants emissions were analyzed. The results indicated that the total pollutants emission amount control and the expected revenue risk can be used as effective measures for regional industry structure adjustment from terminal environmental and macro-economic perspective. The model has a significant value for regional industrial water optimization allocation under uncertainty to achieve the maximum economic benefits and the effective utilization of multiple water resources.

[1]  Bryan Timothy C. Tiu,et al.  An MILP model for optimizing water exchanges in eco-industrial parks considering water quality , 2017 .

[2]  Ping Guo,et al.  Multiobjective Stochastic Fractional Goal Programming Model for Water Resources Optimal Allocation among Industries , 2016 .

[3]  Vladimir Simic,et al.  A multi-stage interval-stochastic programming model for planning end-of-life vehicles allocation , 2016 .

[4]  Guohe Huang,et al.  An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part II. Case study , 2011 .

[5]  Y. P. Li,et al.  Planning Regional Water Resources System Using an Interval Fuzzy Bi-Level Programming Method , 2010 .

[6]  Yanpeng Cai,et al.  A non-probabilistic programming approach enabling risk-aversion analysis for supporting sustainable watershed development , 2016 .

[7]  J. B. Cruz,et al.  Fuzzy input–output model for optimizing eco-industrial supply chains under water footprint constraints , 2011 .

[8]  Jian-xia Chang,et al.  Bi-level optimization allocation model of water resources for different water industries , 2014 .

[9]  Guohe Huang,et al.  Inexact Two-Stage Stochastic Programming for Water Resources Allocation under Considering Demand Uncertainties and Response—A Case Study of Tianjin, China , 2017 .

[10]  J. Diamond,et al.  China's environment in a globalizing world , 2005, Nature.

[11]  Min Zhou,et al.  Modeling for Environmental-Economic Management Systems under Uncertainty , 2010 .

[12]  Sanjay K. Jain,et al.  Modelling of streamflow and its components for a large Himalayan basin with predominant snowmelt yields , 2003 .

[13]  Ping Guo,et al.  Optimization of the industrial structure facing sustainable development in resource-based city subjected to water resources under uncertainty , 2013, Stochastic Environmental Research and Risk Assessment.

[14]  Bellie Sivakumar,et al.  Global climate change and its impacts on water resources planning and management: assessment and challenges , 2011 .

[15]  Lachun Wang,et al.  Optimization of industry structure based on water environmental carrying capacity under uncertainty of the Huai River Basin within Shandong Province, China , 2016 .

[16]  Guohe Huang,et al.  Multi-Source Multi-Sector Sustainable Water Supply Under Multiple Uncertainties: An Inexact Fuzzy-Stochastic Quadratic Programming Approach , 2012, Water Resources Management.

[17]  Yi-Ming Wei,et al.  Operational and environmental performance in China's thermal power industry: Taking an effectiveness measure as complement to an efficiency measure. , 2017, Journal of environmental management.

[19]  Min Zhou,et al.  Optimizing the industrial structure of a watershed in association with economic–environmental consideration: an inexact fuzzy multi-objective programming model , 2013 .

[20]  Kishalay Mitra,et al.  Multiobjective optimization of an industrial grinding operation under uncertainty , 2009 .

[21]  Xiaosheng Qin,et al.  Analyzing urban water supply through an acceptability-index-based interval approach , 2011 .

[22]  Guohe Huang,et al.  An inexact programming approach for supporting ecologically sustainable water supply with the consideration of uncertain water demand by ecosystems , 2011 .

[23]  Hao Yu,et al.  A carbon-constrained stochastic optimization model with augmented multi-criteria scenario-based risk-averse solution for reverse logistics network design under uncertainty , 2017 .

[24]  Jolanta Dvarioniene,et al.  Integrated water resource management model for process industry in Lithuania , 2007 .

[25]  Guohe Huang,et al.  AN INEXACT TWO-STAGE STOCHASTIC PROGRAMMING MODEL FOR WATER RESOURCES MANAGEMENT UNDER UNCERTAINTY , 2000 .

[26]  G H Huang,et al.  A fractional-factorial probabilistic-possibilistic optimization framework for planning water resources management systems with multi-level parametric interactions. , 2016, Journal of environmental management.

[27]  Robert L. Smith,et al.  Capacity Expansion Under Stochastic Demands , 1992, Oper. Res..

[28]  Tong Wang,et al.  Optimization of industrial structure based on water environmental carrying capacity in Tieling City. , 2015, Water science and technology : a journal of the International Association on Water Pollution Research.

[29]  Hella Tokos,et al.  Development of a MINLP model for the optimization of a large industrial water system , 2011 .

[30]  J. Birge,et al.  A multicut algorithm for two-stage stochastic linear programs , 1988 .

[31]  Raymond R. Tan,et al.  Emergy-based fuzzy optimization approach for water reuse in an eco-industrial park , 2011 .

[32]  G H Huang,et al.  An inexact chance-constrained programming model for water quality management in Binhai New Area of Tianjin, China. , 2011, The Science of the total environment.

[33]  Silvia Albrizio,et al.  Environmental policies and productivity growth: Evidence across industries and firms , 2017 .

[34]  Guohe Huang,et al.  A multistage stochastic robust optimization model with fuzzy probability distribution for water supply management under uncertainty , 2015, Stochastic Environmental Research and Risk Assessment.

[35]  Ping Guo,et al.  Optimization of Industrial Structure Considering the Uncertainty of Water Resources , 2013, Water Resources Management.

[36]  Bin Zhang,et al.  Effective MILP model for oil refinery-wide production planning and better energy utilization , 2007 .