Long-term panning of waste diversion under interval and probabilistic uncertainties

Su, Jing; Huang, Guohe; Xi, Beidou; Qin, Xiaosheng; Huo, Shou liang; Jiang, Yonghai; Chen, X.R.

doi:10.1016/j.resconrec.2009.09.011

Cited by 14 publications

(5 citation statements)

References 18 publications

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“…Three waste management scenarios under lowest, medium, and highest diversion rates in Beijing were designed and evaluated through a fuzzy MCDA model. Su et al (2010) developed an inexact chance-constraint mixed integer linear programming (ICMILP) model for supporting waste management in Foshan, China. The ICMILP model can tackle uncertainties presented as intervals and probabilities, facilitate long-term capacity planning, and formulate policies regarding waste generation, collection, transportation and treatment.…”

Section: Chance-constrained Programmingmentioning

confidence: 99%

Applications of inexact programming methods to waste management under uncertainty: current status and future directions

Sun

et al. 2014

Environ Syst Res

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Waste management problems are subject to uncertainties presented as intervals, random variables and/or fuzzy sets. During the past 20 years, inexact programming methods have been developed and applied increasingly to waste management problems under uncertainty. To obtain a snapshot of these studies, this paper gives a review on recent developments, applications, challenges, and barriers associated with inexact programming techniques in supporting waste management. The results indicate that the majority of inexact programming methods can be categorized as two-stage stochastic programming, chance-constrained programming, fuzzy flexible programming, fuzzy robust programming, interval-parameter programming, mixed-integer programming, multiple-objective programming, and nonlinear programming. The demanding areas for future research efforts would include: expansion of conventional concepts to quantify uncertainties, integration of single inexact programming method with other programming methods to deal with multiple uncertainties and even complexities (e.g. nonlinearities and interactions), integration of inexact programming with other modeling techniques (e.g. life cycle assessment, multiple-criteria decision analyses, and waste flow simulation) to support sustainable waste management, development of more efficient algorithms to solve the proposed methods, linkage of waste management with its environmental impacts (e.g. air pollutants and GHG emissions as well as leachate pollution) within an inexact optimization framework, and applications of the developed methods to novel (e.g. specific types of wastes) or real-world waste management cases in different countries.

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Section: Chance-constrained Programmingmentioning

confidence: 99%

Applications of inexact programming methods to waste management under uncertainty: current status and future directions

Sun

et al. 2014

Environ Syst Res

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“…Suprajitno and Mohd 14 presented some interval linear programming problems, where the coefficients and variables are in the form of intervals. Su et al 15 presented an inexact chanceconstraint mixed integer linear programming model for supporting long-term planning of waste management in the City of Foshan, China. A new class of fuzzy stochastic optimization models called two-stage fuzzy stochastic programming with value-at-risk criteria is presented by 16 .…”

Section: Literature Reviewmentioning

confidence: 99%

Multiobjective Two-Stage Stochastic Programming Problems with Interval Discrete Random Variables

Barik

Biswal

Chakravarty

2012

Advances in Operations Research

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Most of the real-life decision-making problems have more than one conflicting and incommensurable objective functions. In this paper, we present a multiobjective two-stage stochastic linear programming problem considering some parameters of the linear constraints as interval type discrete random variables with known probability distribution. Randomness of the discrete intervals are considered for the model parameters. Further, the concepts of best optimum and worst optimum solution are analyzed in two-stage stochastic programming. To solve the stated problem, first we remove the randomness of the problem and formulate an equivalent deterministic linear programming model with multiobjective interval coefficients. Then the deterministic multiobjective model is solved using weighting method, where we apply the solution procedure of interval linear programming technique. We obtain the upper and lower bound of the objective function as the best and the worst value, respectively. It highlights the possible risk involved in the decision-making tool. A numerical example is presented to demonstrate the proposed solution procedure.

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“…For example, Benítez et al 14 develop mathematical models that correlate MSW generation per capita with education, household income, and population. Similar studies cover different regions of the world, like, for example, Port Said‐Egypt, 15 Alleghany County‐United States, 16 Foshan‐China, 17,18 Beijing‐China, 19 Hong Kong, 20 Brazil, 21 and Italy 22 . Most of these papers contain formulations of MIP.…”

Section: Introductionmentioning

confidence: 99%

An optimization‐based analysis of waste to energy options for different income level countries

2021

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Summary Utilizing waste‐to‐energy (WtE) technologies is becoming crucial in today's world where energy sources are scarce. Despite the fact that WtE technologies (incineration, gasification, pyrolysis, anaerobic digestion, and landfill gas recovery) have been analyzed thoroughly in the literature regarding their efficiency rates in treating waste, the applicability of each method to different waste compositions and in various economic environments has not been considered before. In this study, this issue is investigated by modeling and solving a mixed integer programming model. The model is illustrated in three settings, namely Turkey, Brazil, and Germany, each of which is an example of a lower middle income, upper middle income, and a high income country, respectively. The findings of the optimization model suggest that plasma‐arc gasification and advanced incineration stand out as the most efficient technologies to create the WtE conversion, provided that there are sufficient funds to build and run these facilities. If there are economic restraints, anaerobic digestion could be a more cost‐effective way to create energy from waste. However, the solutions can be highly dependent on the parameters of the problem, as indicated in the results of the sensitivity analysis performed. In particular, if CO2 emissions are a big concern, the optimization model favors more of plasma‐arc and pyrolysis technologies. Novelty Statement Despite a wide range of previous studies involving technical analysis of WtE technologies, an economic perspective involving facility building decisions comparing different income level countries has not been performed before. This study examines all waste‐to‐energy methods in the literature not only regarding their efficiency rates in treating waste but also selecting the most appropriate methodology in different settings. In this manner, considering different income level countries brings a proper perspective and novelty to the study.

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Long-term panning of waste diversion under interval and probabilistic uncertainties

Cited by 14 publications

References 18 publications

Applications of inexact programming methods to waste management under uncertainty: current status and future directions

Applications of inexact programming methods to waste management under uncertainty: current status and future directions

Multiobjective Two-Stage Stochastic Programming Problems with Interval Discrete Random Variables

An optimization‐based analysis of waste to energy options for different income level countries

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