G
Guohe Huang
Researcher at Applied Science Private University
Publications - 1071
Citations - 30520
Guohe Huang is an academic researcher from Applied Science Private University. The author has contributed to research in topics: Stochastic programming & Fuzzy logic. The author has an hindex of 72, co-authored 979 publications receiving 25589 citations. Previous affiliations of Guohe Huang include Peking University & Beijing Normal University.
Papers
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A grey linear programming approach for municipal solid waste management planning under uncertainty
TL;DR: In this paper, a grey linear programming (GLP) model is introduced to the civil engineering area, which allows uncertainties in the model inputs to be communicated into the optimization process, and thereby solutions reflecting the inherent uncertainties can be derived.
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An inexact two-stage stochastic programming model for water resources management under uncertainty
Guohe Huang,Daniel P. Loucks +1 more
TL;DR: The ITSP is applied to a hypothetical case study of water resources system operation and results indicate that reasonable solutions have been obtained and the information obtained can provide useful decision support for water managers.
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Optimization of conversion of waste rapeseed oil with high FFA to biodiesel using response surface methodology.
TL;DR: In this paper, waste rapeseed oil with high free fatty acids (FFA) was used as feedstock for producing biodiesel and the response surface methodology was used to optimize the conditions for the maximum conversion to biodiesel.
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A system dynamics approach for regional environmental planning and management: a study for the Lake Erhai Basin.
TL;DR: An environmental system dynamics model, named ErhaiSD, is developed for supporting this planning task of effective planning for the Lake Erhai Basin, China, and the results are directly useful for simulating and evaluating a variety of decision actions and their dynamic consequences.
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An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management
TL;DR: An integration of multi-criteria decision analysis (MCDA) and inexact mixed integer linear programming (IMILP) methods to support selection of an optimal landfill site and a waste-flow-allocation pattern such that the total system cost can be minimized.