Grey Quadratic Programming and Its Application to Municipal Solid Waste Management Planning Under Uncertainty

Huang, Guohe; Baetz, Brian W.; Patry, Gilles G.

doi:10.1080/03052159508941354

Cited by 98 publications

(86 citation statements)

References 14 publications

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“…Due to the lack of generally applicable algorithms for handling the nonlinear structure and the inexact information embedded in the structure, most nonlinear programming problems are difficult to solve. The IBA method proposed in [11,22] is not intended for dealing with generic nonlinear problems. In contrast, the GA-based method can be used as a general problem solver for this type of problems because there is not much difference for GA between treating the term of In the following, a computation experiment will be conducted to illustrate how the GAINLP method can handle complicated inexact nonlinear problems.…”

Section: Ga-based Methods For Solving Inexact Nonlinear Problems (Gainlp)mentioning

confidence: 99%

“…For decision makers, it is usually more feasible to represent uncertain information as inexact data than to specify distributions of fuzzy sets or probability functions. Hence, various kinds of inexact programming such as ILP, IQP, inexact integer programming (IIP), inexact dynamic programming (IDP) and inexact multiobjective programming (IMOP) have been developed and are well discussed [10,11,19]. It can be observed from these studies that applications of inexact models to practical solid waste planning systems are effective.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…The primary considerations involved include cost control, environmental sustainability and waste reutilization. The techniques employed include linear programming [2][3][4][5], mixed integer linear programming [6], multiobjective programming [7][8][9], nonlinear programming [10,11], as well as their hybrids, which involve probability, fuzzy set and inexact analysis [12][13][14][15][16]. Due to complexity of the nonlinear programming problems for solid waste management, research works in the area are scant; some exceptions include [17,18].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Genetic Algorithm-Based Approaches for Solving Inexact Optimization Problems and their Applications for Municipal Solid Waste Management

Jin¹,

Hu²,

Chan³

2016

Optimization Algorithms - Methods and Applications

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This chapter proposes a genetic algorithm (GA)-based approach as an all-purpose problem-solving method for optimization problems with uncertainty. This chapter explains the GA-based method and presents details on the computation procedures involved for solving the three types of inexact optimization problems, which include the ILP, inexact quadratic programming (IQP) and inexact nonlinear programming (INLP) optimization problems.In the three-stage GA-based method for solution of ILP problems, also called GAILP, the upper and lower bounds of the inexact numbers of coefficients can be calculated directly without any uncertainty in the coefficients by substituting the initial suboptimal decision variables into the objective function. The GAILP has been extended to solve the IQP problems and the more complicated INLP problems. The implementation of these approaches was performed using the Genetic Algorithm Solver of MATLAB.The proposed GA-based approaches were applied for management of a set of case scenarios related to municipal solid waste management. A comparison of the results generated by the proposed GA-based optimization approach with those produced by the traditional interactive binary analysis method reveals that the proposed approach has fewer limitations and involves less complex procedures in solving the inexact optimization problems.

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Section: Ga-based Methods For Solving Inexact Nonlinear Problems (Gainlp)mentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Genetic Algorithm-Based Approaches for Solving Inexact Optimization Problems and their Applications for Municipal Solid Waste Management

Jin¹,

Hu²,

Chan³

2016

Optimization Algorithms - Methods and Applications

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“…Although the IMP proves to be an effective approach in dealing with uncertainties, it encounters difficulties when the model's right-hand-side coefficients are highly uncertain. Several integrated IMP, FMP, and/or SMP methods were developed to tackle such a difficulty (Huang et al 1993(Huang et al , 1994b(Huang et al , 1995aZou et al 2000;Luo et al 2003;Maqsood and Huang 2003). Among them, interval-fuzzy linear programming (IFLP) (Huang et al 1993;Wang and Huang 2013a, b;Hu et al 2014;Li et al 2014), which is a hybrid of interval linear programming (ILP) and flexible fuzzy linear programming (FLP), is useful in accounting for uncertainties expressed as discrete intervals and/or fuzzy membership functions.…”

Section: Introductionmentioning

confidence: 99%

Trade-off between water pollution prevention, agriculture profit, and farmer practice—an optimization methodology for discussion on land-use adjustment in China

Liu

Zhang

Zhang³

et al. 2014

Environ Monit Assess

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Agricultural decision-making to control nonpoint source (NPS) water pollution may not be efficiently implemented, if there is no appropriate cost-benefit analysis on agricultural management practices. This paper presents an interval-fuzzy linear programming (IFLP) model to deal with the trade-off between agricultural revenue, NPS pollution control, and alternative practices through land adjustment for Wuchuan catchment, a typical agricultural area in Jiulong River watershed, Fujian Province of China. From the results, the lower combination of practice 1, practice 2, practice 3, and practice 7 with the land area of 12.6, 5.2, 145.2, and 85.3 hm 2 , respectively, could reduce NPS pollution load by 10 %. The combination yields an income of 98,580 Chinese Yuan/a. If the pollution reduction is 15 %, the higher combination need practice 1, practice 2, practice 3, practice 5, and practice 7 with the land area of 54.4, 23.6, 18.0, 6.3, and 85.3 hm 2 , respectively. The income of this combination is 915,170 Chinese Yuan/a. The sensitivity analysis of IFLP indicates that the cost-effective practices are ranked as follows: practice 7 > practice 2 > practice 1 > practice 5 > practice 3 > practice 6 > practice 4. In addition, the uncertainties in the agriculture NPS pollution control system could be effectively quantified by the IFLP model. Furthermore, to accomplish a reasonable and applicable project of land-use adjustment, decisionmakers could also integrate above solutions with their own experience and other information.

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“…For soil erosion, the universal soil loss equation (USLE) (Renard et al, 1991) was used to estimate the effects of six factors-rainfall erosivity, soil erodibility, slope length, slope steepness, cover and management, and conservation practices-on annual sheet and rill erosion. The USLE has been customized for detailed basin studies, to predict soil loss under different hydrological, climatological and landscape conditions (Huang et al, 1995a). Since the majority of parameters related to soil loss are uncertain in their nature, probabilistic and inexact analyses for the uncertainties were undertaken throughout the modelling process.…”

Section: Simulation Modellingmentioning

confidence: 99%

A hybrid GIS-supported watershed modelling system: application to the Lake Erhai basin, China

Huang

Liu

Chakma

et al. 1999

Hydrological Sciences Journal

Self Cite

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In this study, a hybrid GIS-supported watershed modelling system is developed for integrated planning of water pollution control in the Lake Erhai basin, China. The system comprises a GIS-supported database, simulation/optimization models, and user interfaces developed through Arc/Info and ArcView. An inexactfuzzy multi-objective optimization model is developed for assessing a variety of system objectives and generating desired decision alternatives. It can effectively reflect uncertain, interactive and multi-objective features of the study system, and its solutions provide decision makers with a flexible decision space. The GIS technology is used for managing spatial and non-spatial data, linking models, and providing interfaces between the models and their users. The developed modelling system allows regional planners to obtain desired plans of human activities under a variety of objectives and constraints and to examine consequences and impacts resulting from alternative management practices.

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Grey Quadratic Programming and Its Application to Municipal Solid Waste Management Planning Under Uncertainty

Cited by 98 publications

References 14 publications

Genetic Algorithm-Based Approaches for Solving Inexact Optimization Problems and their Applications for Municipal Solid Waste Management

Genetic Algorithm-Based Approaches for Solving Inexact Optimization Problems and their Applications for Municipal Solid Waste Management

Trade-off between water pollution prevention, agriculture profit, and farmer practice—an optimization methodology for discussion on land-use adjustment in China

A hybrid GIS-supported watershed modelling system: application to the Lake Erhai basin, China

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