ITCLP: An inexact two-stage chance-constrained program for planning waste management systems

Li, Y. P.; Huang, Guohe; Nie, Songlin; Qin, Xiaosheng

doi:10.1016/j.resconrec.2006.03.017

Cited by 110 publications

(42 citation statements)

References 35 publications

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“…CCP solves the above model by converting it into a deterministic version through (1) fixing a certain level of probability q i (q i ʦ [0,1]) for uncertain constraint i, which represents the admissible risk of violating constraint i; and (2) imposing the condition that the constraint should be satisfied with at least a probability level of 1 Ϫ q i . The feasible solution set is thus subject to the following constraints 10,31 :…”

Section: Methodsmentioning

confidence: 99%

“…If both left-and right-hand sides (A and B) are uncertain, the set of feasible constraints may become more complicated; this brings about considerable difficulty to its practical application, particularly for largescale problems. 10,31,32 On the other hand, in many realworld situations, the distribution information may hardly be known, whereas only two bounds of each imprecise parameter can be identified as intervals. Therefore, for reflecting uncertainties presented as intervals (in A and C), an extended consideration is the introduction of IPP technique into the CCP framework.…”

Section: Methodsmentioning

confidence: 99%

“…However, in real-world MSW management problems, uncertainties exist in the related costs, impact factors, and objectives that may affect the optimization processes and the decision schemes generated. 10 The complexities could be further amplified by interactions among the uncertain parameters and through additional economic implications. Such complexities have placed many MSW management problems beyond deterministic programming methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling Municipal Solid Waste Management System under Uncertainty

Huang

2010

Journal of the Air & Waste Management Association

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In this study, a dynamic inexact waste management (DIWM) model is developed for identifying optimal waste-flow-allocation and facility-capacity-expansion strategies under uncertainty and is based on an inexact scenario-based probabilistic programming (ISPP) approach. The DIWM model can handle uncertainties presented as interval values and probability distributions, and it can support assessing the risk of violating system constraints. Several violation levels for facility-capacity and waste-diversion constraints are examined. Solutions associated with different risks of constraint violation were generated. The modeling results are valuable for supporting the planning of the study city's municipal solid waste (MSW) management practices, the long-term capacity expansion for waste management system, and the identification of desired policies regarding waste diversion. Sensitivity analyses are also undertaken to demonstrate that the violations of different constraints have varied effects on the planning of waste-flow allocation, facility expansion, and waste management cost.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Municipal Solid Waste Management System under Uncertainty

Huang

2010

Journal of the Air & Waste Management Association

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“…The fundamental idea behind the two-stage programming is the concept of recourse, which defines the ability to take corrective actions after a random event having taken place (Li et al, 2007). This study deals with the uncertainties of coming from biogas yield (PGR) and manure generation rate (PDV).…”

Section: Two-stage Optimizationmentioning

confidence: 99%

Planning of methane emission control from hoggery using an inexact two-stage optimization model

Chen

2014

Journal of the Air & Waste Management Association

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This study aims to develop an inexact two-stage optimization model to gather manure distributed over the southwest Taiwan and convert it into bioenergy. In the method, local optimization of each hauling zone is performed first using a gray mixed-integer programming model. Then, the hauling zones are prioritized by its performance on four gray scenarios. Although the biogas yield and the manure generation rate are ambiguous, one can easily evaluate his opportunity and risk by gray interval, which is a group of values within the lower and upper bounds. The analyses reveal that the biogas yield dominates the profit in this project, and it leads to the failure of the project when the biogas yield is below the level of 0.2 m 3 kg À1. With the goal of reducing 45% of methane emissions from pig farms, seven hauling zones are required to be developed. The farmers living in these zones from the project get carbon credits ranging from 478 to 3269 ton CO 2eq per year, and the investors own the carbon credits in the range of 3264-11820 ton CO 2eq per year. Through the carbon trading, both the investors and pig farmers are able to make profits by trading their carbon credits.Implications: Biogas recovered from hoggery can be used as a bioenergy source and mitigate the atmospheric greenhouse effect and global warming. This research develops an inexact two-stage optimization model to evaluate the potential of gathering manure for biogas and converting it into bioenergy. The analyses reveal that the biogas yield dominates the profit in this project, and it leads to the failure of the project when the biogas yield is below the level of 0.2 m 3 kg À1 . This study has provided a useful reference for the management of biogas production and carbon trading from hoggery for bioenergy.

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“…Uncertainties that are presented as random information exist especially in the hydrological cycle, variation in the water environment over time and space, or estimation errors on parameters [10][11][12]. Chance-constrained programming (CCP) is the most typical stochastic mathematical programming (SMP) method to present and deal with uncertainty expressed as random information, which can be easily integrated with other optimization approaches to deal with multiform uncertainties [1,13,14]. However, few researches have been reported that can represent random variables in the double-hand-side of the constraints applied to the optimization problem in ecosystem services.…”

Section: Introductionmentioning

confidence: 99%

Application of a Double-Sided Chance-Constrained Integer Linear Program for Optimization of the Incremental Value of Ecosystem Services in Jilin Province, China

Cai

Wang

2017

Water

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Abstract:The Interconnected River System Network Project (IRSNP) is a significant water supply engineering project, which is capable of effectively utilizing flood resources to generate ecological value, by connecting 198 lakes and ponds in western Jilin, northeast China. In this article, an optimization research approach has been proposed to maximize the incremental value of IRSNP ecosystem services. A double-sided chance-constrained integer linear program (DCCILP) method has been proposed to support the optimization, which can deal with uncertainties presented as integers or random parameters that appear on both sides of the decision variable at the same time. The optimal scheme indicates that after rational optimization, the total incremental value of ecosystem services from the interconnected river system network project increased 22.25%, providing an increase in benefits of 3.26 × 10 9 ¥ compared to the original scheme. Most of the functional area is swamp wetland, which provides the greatest ecological benefits. Adjustment services increased obviously, implying that the optimization scheme prioritizes ecological benefits rather than supply and production services.Keywords: incremental value of ecosystem service; double-sided chance-constrained integer linear program; interconnected river system network project; optimization; utilization of flood resource

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ITCLP: An inexact two-stage chance-constrained program for planning waste management systems

Cited by 110 publications

References 35 publications

Modeling Municipal Solid Waste Management System under Uncertainty

Modeling Municipal Solid Waste Management System under Uncertainty

Planning of methane emission control from hoggery using an inexact two-stage optimization model

Application of a Double-Sided Chance-Constrained Integer Linear Program for Optimization of the Incremental Value of Ecosystem Services in Jilin Province, China

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