1995
DOI: 10.1016/0377-2217(94)00093-r
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Grey integer programming: An application to waste management planning under uncertainty

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Cited by 363 publications
(245 citation statements)
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“…An inexact-stochastic model is based on an inexact chance-constrained programming method, which improves upon the existing inexact and stochastic programming approaches by allowing both distribution information in the left hand and uncertainties in the right hand to be effectively incorporated within its optimization process (Huang et al 1995b). Thus, decision alternatives can be generated by adjusting decision variable values within their solution intervals.…”
Section: Discussionmentioning
confidence: 99%
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“…An inexact-stochastic model is based on an inexact chance-constrained programming method, which improves upon the existing inexact and stochastic programming approaches by allowing both distribution information in the left hand and uncertainties in the right hand to be effectively incorporated within its optimization process (Huang et al 1995b). Thus, decision alternatives can be generated by adjusting decision variable values within their solution intervals.…”
Section: Discussionmentioning
confidence: 99%
“…The IFLP allows the interval information to be directly communicated into the optimization process and resulting solution (Huang et al 1995b;Wang and Huang 2013a, b;Hu et al 2014). The abstract function of system design model based on an interval number optimization can be written as follows (Huang et al 1993;Liu et al 2006):…”
Section: Iflp Model Formulationmentioning
confidence: 99%
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“…An interactive two-step algorithm is developed to solve the above problem through analyzing the detailed interrelationships between the parameters and the variables and between the objective function and the constraints [36,41]. The submodel for l þ corresponding to f À can be formulated in the first step when the system objective is to be minimized; the other submodel for l À can then be formulated based on the solution of the first submodel.…”
Section: Methodsmentioning
confidence: 99%
“…Intervalparameter programming (IPP) is an alternative for tackling uncertainties expressed as intervals that exist in the model's leftand/or right-hand sides as well as the objective function (Suo et al, 2013). The IPP has advantages such that (i) it allows uncertainties to be directly communicated into the optimization process, (ii) it does not lead to more complicated intermediate models, and thus has a relatively low computational requirement and (iii) it does not require distributional information for model parameters, which is particularly meaningful for practical applications because it is typically much more difficult for planners/engineers to specify distributions than to obtain intervals (Huang et al, 1995;Zhang et al, 2014). An IPP model can be formulated as follows:…”
Section: Methodsmentioning
confidence: 99%