A Stone Resource Assignment Model under the Fuzzy Environment
This paper proposes a bilevel multiobjective optimization model with fuzzy coefficients to tackle a stone resource assignment problem with the aim of decreasing dust and waste water emissions. On the upper level, the local government wants to assign a reasonable exploitation amount to each stone pla...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2012-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2012/265837 |
id |
doaj-191afe368ecf46ec9fc3524a4dda7e18 |
---|---|
record_format |
Article |
spelling |
doaj-191afe368ecf46ec9fc3524a4dda7e182020-11-24T21:54:34ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472012-01-01201210.1155/2012/265837265837A Stone Resource Assignment Model under the Fuzzy EnvironmentLiming Yao0Jiuping Xu1Feng Guo2Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, ChinaUncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, ChinaUncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, ChinaThis paper proposes a bilevel multiobjective optimization model with fuzzy coefficients to tackle a stone resource assignment problem with the aim of decreasing dust and waste water emissions. On the upper level, the local government wants to assign a reasonable exploitation amount to each stone plant so as to minimize total emissions and maximize employment and economic profit. On the lower level, stone plants must reasonably assign stone resources to produce different stone products under the exploitation constraint. To deal with inherent uncertainties, the object functions and constraints are defuzzified using a possibility measure. A fuzzy simulation-based improved simulated annealing algorithm (FS-ISA) is designed to search for the Pareto optimal solutions. Finally, a case study is presented to demonstrate the practicality and efficiency of the model. Results and a comparison analysis are presented to highlight the performance of the optimization method, which proves to be very efficient compared with other algorithms.http://dx.doi.org/10.1155/2012/265837 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liming Yao Jiuping Xu Feng Guo |
spellingShingle |
Liming Yao Jiuping Xu Feng Guo A Stone Resource Assignment Model under the Fuzzy Environment Mathematical Problems in Engineering |
author_facet |
Liming Yao Jiuping Xu Feng Guo |
author_sort |
Liming Yao |
title |
A Stone Resource Assignment Model under the Fuzzy Environment |
title_short |
A Stone Resource Assignment Model under the Fuzzy Environment |
title_full |
A Stone Resource Assignment Model under the Fuzzy Environment |
title_fullStr |
A Stone Resource Assignment Model under the Fuzzy Environment |
title_full_unstemmed |
A Stone Resource Assignment Model under the Fuzzy Environment |
title_sort |
stone resource assignment model under the fuzzy environment |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2012-01-01 |
description |
This paper proposes a bilevel multiobjective optimization model with fuzzy coefficients
to tackle a stone resource assignment problem with the aim of decreasing dust and waste water emissions. On the upper level, the local government wants to assign a reasonable exploitation amount to each stone plant so as to minimize total emissions and maximize employment and economic profit. On the lower level, stone plants must reasonably assign stone resources to produce different stone products under the exploitation constraint. To deal with inherent uncertainties, the object functions and constraints are defuzzified using a possibility measure. A fuzzy simulation-based improved simulated annealing algorithm (FS-ISA) is designed to search for the Pareto optimal solutions. Finally, a case study is presented to demonstrate the practicality and efficiency of the model. Results and a comparison analysis are presented to highlight the performance of the optimization method, which proves to be very efficient compared with other algorithms. |
url |
http://dx.doi.org/10.1155/2012/265837 |
work_keys_str_mv |
AT limingyao astoneresourceassignmentmodelunderthefuzzyenvironment AT jiupingxu astoneresourceassignmentmodelunderthefuzzyenvironment AT fengguo astoneresourceassignmentmodelunderthefuzzyenvironment AT limingyao stoneresourceassignmentmodelunderthefuzzyenvironment AT jiupingxu stoneresourceassignmentmodelunderthefuzzyenvironment AT fengguo stoneresourceassignmentmodelunderthefuzzyenvironment |
_version_ |
1725867145176285184 |