A case-based reasoning method for capacity identification in the Choquet integral: Application to cyclic construction operations

被引:5
作者
Dhouib, Diala [1 ,2 ]
Chabchoub, Habib [3 ,4 ]
机构
[1] Super Inst Ind Management Sfax, Dept Quantitat Methods, Sfax, Tunisia
[2] Res Unit LOGIQ, Sfax 3018, Tunisia
[3] Fac Econ & Management Sfax, Dept Quantitat Methods, Sfax, Tunisia
[4] Res Unit GIAD, Sfax, Tunisia
关键词
LINGUISTIC REPRESENTATION MODEL; INTERACTING CRITERIA; AGGREGATION; INFORMATION; SYSTEM;
D O I
10.1016/j.jmsy.2011.01.004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The estimation of productivity rates in cyclic construction processes is a crucial task in the planning of construction projects. Since the productivity depends on many factors, the main ones should be selected by taking into account the possible interactions between them. For this aim, we adopted the Choquet integral to define fuzzy weights of criteria or coalition of criteria called capacities. In the literature, many approaches of capacity identification in the Choquet integral are proposed. However, these approaches require information, which is always hardly provided by the decision-maker and necessitates the determination of utility functions. Furthermore, they usually deal with a set of objects having a small cardinality. In this study, we use the Case-Based Reasoning (CBR) for capacity identification in concreting operations by developing a new quadratic program. By this way, a selection of "major" criteria is first performed to which a 2-tuple linguistic representation model is applied in order to unify quantitative and qualitative information. Then, a causal model is used to estimate the overall score of the new case. As this work demonstrates, the developed methodology can provide a good tool for capacity identification that is able to justify its choices clearly and consistently in the concreting operations. (C) 2011 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:151 / 163
页数:13
相关论文
共 40 条
[1]  
AAMODT A, 1994, AI COMMUN, V7, P39
[2]  
ADDOUCHE S, 2006, J EUR SYST AUTOM, V40, P33, DOI DOI 10.3166/JESA.33-50
[3]  
Ahn H, 2006, EXPERT SYSTEMS APPL, DOI DOI 10.1016/J.ESWA.2006.06.021
[4]  
[Anonymous], 1997, APPL CASE BASED REAS
[5]  
[Anonymous], P 5 FUZZ SYST S JAP
[6]  
[Anonymous], 1993, Case-Based Reasoning
[7]   Comparison of case-based reasoning and artificial neural networks [J].
Arditi, D ;
Tokdemir, OB .
JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 1999, 13 (03) :162-169
[8]  
BONISSONE P, 1986, UNCERTAINTY ARTIFICI
[9]  
CARDIE C, 1993, P 10 INT C MACH LEAR, P25
[10]  
Cardie C., 1997, Proceedings of the Fourteenth International Conference on Machine Learning, P57