Title | A fuzzy-robust stochastic multiobjective programming approach for petroleum waste management planning |
Authors | Zhang, Xiaodong Huang, Guo H. Chan, Christine W. Liu, Zhenfang Lin, Qianguo |
Affiliation | Univ Regina, Fac Engn & Appl Sci, Regina, SK S4S 0A2, Canada. Peking Univ, Coll Urban Environm Sci, Beijing 100871, Peoples R China. |
Keywords | Multiobjective programming Stochastic Fuzzy Robust Petroleum waste management INTERACTIVE SATISFICING METHOD SOLID-WASTE QUALITY MANAGEMENT DECISION-MAKING LINEAR-PROGRAMS MODEL OPTIMIZATION UNCERTAINTY SYSTEMS PARAMETERS |
Issue Date | 2010 |
Publisher | applied mathematical modelling |
Citation | APPLIED MATHEMATICAL MODELLING.2010,34,(10),2778-2788. |
Abstract | This paper proposes a fuzzy-robust stochastic multiobjective programming (FRSMOP) approach, which integrates fuzzy-robust linear programming and stochastic linear programming into a general multiobjective programming framework. A chosen number of noninferior solutions can be generated for reflecting the decision-makers' preferences and subjectivity. The FRSMOP method can effectively deal with the uncertainties in the parameters expressed as fuzzy membership functions and probability distribution. The robustness of the optimization processes and solutions can be significantly enhanced through dimensional enlargement of the fuzzy constraints. The developed FRSMOP was then applied to a case study of planning petroleum waste-flow-allocation options and managing the related activities in an integrated petroleum waste management system under uncertainty. Two objectives are considered: minimization of system cost and minimization of waste flows directly to landfill. Lower waste flows directly to landfill would lead to higher system costs due to high transportation and operational costs for recycling and incinerating facilities, while higher waste flows directly to landfill corresponding to lower system costs could not meet waste diversion objective environmentally. The results indicate that uncertainties and complexities can be effectively reflected, and useful information can be generated for providing decision support. (C) 2009 Elsevier Inc. All rights reserved. |
URI | http://hdl.handle.net/20.500.11897/242315 |
ISSN | 0307-904X |
DOI | 10.1016/j.apm.2009.12.012 |
Indexed | SCI(E) EI |
Appears in Collections: | 待认领 |