The Experts below are selected from a list of 31659 Experts worldwide ranked by ideXlab platform
G H Huang - One of the best experts on this subject based on the ideXlab platform.
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interval fuzzy robust dynamic programming for nonrenewable energy resources management with chance constraints
Energy Sources Part B-economics Planning and Policy, 2014Co-Authors: G H Huang, Ying LiAbstract:This study introduces a chance constrained interval fuzzy robust dynamic programming (CCIFRDP) approach, which can effectively reflect uncertain, dynamic and interactive features of energy-environmental management systems, as well as assist in examining the reliability of satisfying (or risk of violating) system constraints under uncertainty. Within a multi-stage context, the CCIFRDP can facilitate dynamic analysis for capacity-expansion planning under different constraint-violation risk levels. The developed method has been applied to the planning for facility expansion and energy-Flow Allocation within a regional energy-environment system. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different levels of constraint-violation risk. The obtained interval solutions are useful in generating decision alternatives, which represent various options for environmental-economic tradeoffs. The results can be used to generate decision alternatives a...
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a mathematical model for identifying an optimal waste management policy under uncertainty
Applied Mathematical Modelling, 2012Co-Authors: G H Huang, S L NieAbstract:Abstract In the municipal solid waste (MSW) management system, there are many uncertainties associated with the coefficients and their impact factors. Uncertainties can be normally presented as both membership functions and probabilistic distributions. This study develops a scenario-based fuzzy-stochastic quadratic programming (SFQP) model for identifying an optimal MSW management policy and for allowing dual uncertainties presented as probability distributions and fuzzy sets being communicated into the optimization process. It can also reflect the dynamics of uncertainties and decision processes under a complete set of scenarios. The developed method is applied to a case study of long-term MSW management and planning. The results indicate that reasonable solutions have been generated. They are useful for identifying desired waste-Flow-Allocation plans and making compromises among system cost, satisfaction degree, and constraint-violation risk.
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slfp a stochastic linear fractional programming approach for sustainable waste management
Waste Management, 2011Co-Authors: H Zhu, G H HuangAbstract:A stochastic linear fractional programming (SLFP) approach is developed for supporting sustainable municipal solid waste management under uncertainty. The SLFP method can solve ratio optimization problems associated with random information, where chance-constrained programming is integrated into a linear fractional programming framework. It has advantages in: (1) comparing objectives of two aspects, (2) reflecting system efficiency, (3) dealing with uncertainty expressed as probability distributions, and (4) providing optimal-ratio solutions under different system-reliability conditions. The method is applied to a case study of waste Flow Allocation within a municipal solid waste (MSW) management system. The obtained solutions are useful for identifying sustainable MSW management schemes with maximized system efficiency under various constraint-violation risks. The results indicate that SLFP can support in-depth analysis of the interrelationships among system efficiency, system cost and system-failure risk.
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interval parameter semi infinite fuzzy stochastic mixed integer programming approach for environmental management under multiple uncertainties
Waste Management, 2010Co-Authors: Ping Guo, G H HuangAbstract:In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-Flow Allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-Flow Allocation within a multi-facility, multi-period and multi-option context.
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inexact fuzzy stochastic mixed integer programming approach for long term planning of waste management part a methodology
Journal of Environmental Management, 2009Co-Authors: Ping Guo, G H HuangAbstract:In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-Flow Allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada.
Guo H Huang - One of the best experts on this subject based on the ideXlab platform.
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a two stage fuzzy robust integer programming approach for capacity planning of environmental management systems
European Journal of Operational Research, 2008Co-Authors: Y P Li, Guo H HuangAbstract:In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management facilities within a multi-stage context. In the modeling formulation, uncertainties can be presented in terms of both possibilistic and probabilistic distributions, such that robustness of the optimization process could be enhanced. In its solution process, the fuzzy decision space is delimited into a more robust one by specifying the uncertainties through dimensional enlargement of the original fuzzy constraints. The TFRIP method is applied to a case study of long-term waste-management planning under uncertainty. The generated solutions for continuous and binary variables can provide desired waste-Flow-Allocation and capacity-expansion plans with a minimized system cost and a maximized system feasibility.
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grey integer programming an application to waste management planning under uncertainty
European Journal of Operational Research, 1995Co-Authors: Guo H Huang, Brian W Baetz, Gilles G PatryAbstract:Abstract This paper introduces a grey integer programming (GIP) method for facility expansion planning under uncertainty, by incorporating the concepts of grey number and grey mathematical programming into a mixed integer linear programming optimization framework. The approach is an improvement upon previous integer programming methods in terms of its technical characteristics and applicability. It allows uncertain information to be effectively communicated into the optimization process and the resulting solutions. It also has low computational requirements and is thus applicable to practical problems. The modelling approach is applied to a hypothetical planning problem of waste Flow Allocation and treatment/disposal facility expansion within a regional solid waste management system. The binary variable solutions provide the ranges of different development alternatives within a multi-period, multi-facility and multi-scale context, and the continuous variable solutions provide optimal schemes for waste Flow Allocation corresponding to the upper and lower bounds of the objective function value. The results indicate that reasonable and useful solutions can be achieved through the developed approach.
Ping Guo - One of the best experts on this subject based on the ideXlab platform.
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interval parameter semi infinite fuzzy stochastic mixed integer programming approach for environmental management under multiple uncertainties
Waste Management, 2010Co-Authors: Ping Guo, G H HuangAbstract:In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-Flow Allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-Flow Allocation within a multi-facility, multi-period and multi-option context.
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inexact fuzzy stochastic mixed integer programming approach for long term planning of waste management part a methodology
Journal of Environmental Management, 2009Co-Authors: Ping Guo, G H HuangAbstract:In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-Flow Allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada.
Y P Li - One of the best experts on this subject based on the ideXlab platform.
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a two stage fuzzy robust integer programming approach for capacity planning of environmental management systems
European Journal of Operational Research, 2008Co-Authors: Y P Li, Guo H HuangAbstract:In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management facilities within a multi-stage context. In the modeling formulation, uncertainties can be presented in terms of both possibilistic and probabilistic distributions, such that robustness of the optimization process could be enhanced. In its solution process, the fuzzy decision space is delimited into a more robust one by specifying the uncertainties through dimensional enlargement of the original fuzzy constraints. The TFRIP method is applied to a case study of long-term waste-management planning under uncertainty. The generated solutions for continuous and binary variables can provide desired waste-Flow-Allocation and capacity-expansion plans with a minimized system cost and a maximized system feasibility.
Hussain Shareef - One of the best experts on this subject based on the ideXlab platform.
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power Flow Allocation method with the application of hybrid genetic algorithm least squares support vector machine
IEEE International Power and Energy Conference, 2010Co-Authors: Mohd Wazir Mustafa, Mohd Herwan Sulaiman, Saifulnizam Abd Khalid, Hussain ShareefAbstract:This paper proposes a new power Flow Allocation method in pool based power system with the application of hybrid genetic algorithm (GA) and least squares support vector machine (LS-SVM), namely GA-SVM. GA is utilized to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ2, which are embedded in LS-SVM model so that the power Flow Allocation problem can be solved by using machine learning adaptation approach. The supervised learning paradigm is used to train the LS-SVM model where the proportional sharing principle (PSP) method is utilized as a teacher. Based on converged load Flow and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The GA-SVM model will learn to identify which generators are supplying to which loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the proposed method. The comparison result with artificial neural network (ANN) technique is also will be presented.
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real and reactive power Flow Allocation in deregulated power system utilizing genetic support vector machine technique
International Review of Electrical Engineering-iree, 2010Co-Authors: Mohd Herwan Sulaiman, Mohd Wazir Mustafa, Omar Aliman, S N Khalid, Hussain ShareefAbstract:This paper presents a technique to allocate the real and reactive power Flow in deregulated power system environment by incorporating the hybridization of Genetic Algorithm and Least Squares Support Vector Machine (Genetic-SVM). The idea is to use GA to find the optimal values of hyper-parameters of LS-SVM and adapt a supervised learning approach to train the LS-SVM model. The manipulation of proportional sharing method (PSM) is utilized as a teacher. Based on converged load Flow and followed by PSM for power Flow Allocation procedures, the description of inputs and outputs of the training data are created. The Genetic-SVM model will learn to identify which generators are supplying to which loads. In addition, the equivalent transmission model will be discussed in reactive power tracing methodology together with the concept of virtual load for both real and reactive power tracing methods. In this paper, 5- bus system and 25-bus equivalent system of southern Malaysia are used to show the effectiveness of the proposed method. The comparison with other method is also given.
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identification of source to sink relationship in deregulated power systems using artificial neural network
International Power Engineering Conference, 2007Co-Authors: Mohd Wazir Mustafa, Hussain Shareef, A Khairuddin, S N KhalidAbstract:This paper suggests a method to identify the relationship of real power transfer between source and sink using artificial neural network (ANN). The basic idea is to use supervised learning paradigm to train the ANN. For that a conventional power Flow tracing method is used as a teacher. Based on solved load Flow and followed by power tracing procedure, the description of inputs and outputs of the training data for the ANN is easily obtained. An artificial neural network is developed to assess which generators are supplying a specific load. Most commonly used feedforward architecture has been chosen for the proposed ANN power transfer Allocation technique. Almost all system variables obtained from load Flow solutions are utilised as an input to the neural network. Moreover, log-sigmoid activation functions are incorporated in the hidden layer to realise the non linear nature of the power Flow Allocation. The proposed ANN provides promising results in terms of accuracy and computation time. The IEEE 14-bus network is utilised as a test system to illustrate the effectiveness of the ANN output compared to that of conventional methods.