Factorial-Based Inexact Stochastic Fuzzy Chance Constraint Programming Framework for Municipal Solid Waste Management with GHG Emission Trading: Analysis of Multilevel Parametric Interactions

Ma, Xiaolin; Ma, Chi; Liu, Hongyu

Factorial-Based Inexact Stochastic Fuzzy Chance Constraint Programming Framework for Municipal Solid Waste Management with GHG Emission Trading: Analysis of Multilevel Parametric Interactions

2016

Ma, Xiaolin | Ma, Chi | Liu, Hongyu

This study proposes a factorial inexact stochastic fuzzy chance constraint programming framework for dealing with uncertainties in municipal solid waste management under consideration of greenhouse gas (GHG) emission trading. It can reflect uncertainties expressed as fuzzy, interval, and random variables and generate desired management strategies for minimizing the integrated cost for solid waste disposal and purchasing GHG emission credit. Moreover, multilevel factorial analysis is conducted to reveal the main and interactive effects of uncertain parameters. The results show that effective waste allocation schemes can be obtained to meet the waste disposal demands and GHG emission requirements under different α-cut levels. The changes in the fuzzy confidence level have impacts on the waste allocation schemes, especially for the waste flow to the incinerator. The disposal cost differs across the three levels of 0.3, 0.5, and 0.7 for incinerator capacity constraint when the fuzzy confidence level of composting capacity constraint is equal to 0.5, implying the existence of the interaction between uncertainties in the incinerator and composting facility. Comparison between the waste management practices with and without considering GHG emission requirements indicates that the purchase of GHG emission credits would contribute about 10 % to the total cost, which would not be influenced significantly by the α-cut level.

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