FCM based hybrid evolutionary computation approach for optimization power consumption by varying cars in EGCS

Chen, Ta-Cheng; Lee, An-Chen; Huang, Shih Lun

doi:10.1016/j.apm.2015.04.025

Cited by 6 publications

(1 citation statement)

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“…These fields include helping to solve the problem by organizing complex data or mass, helping the interview process by increasing understanding and controlling programs and managing large volumes of quality data (Rezaee et al , 2019). Some of FCM’s applications include the prediction (Chen et al , 2015; Rezaee, Yousefi and Hayati, 2018), modeling (Pourreza et al , 2018; Mital et al , 2018), decision making (Alizadeh and Yousefi, 2018; Rezaee, Yousefi, Valipour and Dehdar, 2018), analysis (Bakhtavar and Yousefi, 2018) and classification (Amirkhani et al , 2018).…”

Section: Methodsmentioning

confidence: 99%

Analysing causal relationships between delay factors in construction projects

Rezaee

Yousefi

Chakrabortty

2019

IJMPB

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Purpose Analyzing factors of delays in construction projects and determining their impact on project performance is necessary to better manage and control projects. Identification of root factors which may lead to project delay and increased cost is vital at the early or planning stage. Better identification of delay factors at the early stage can help the practitioners to reduce their impacts over the long run. Hence, the purpose of this paper is to propose an intelligent method to analyze causal relationships between delay factors in construction projects. The proposed approach is further validated by a real case study of the construction projects in West Azerbaijan province in Iran. Design/methodology/approach During the first phase, the fuzzy cognitive map (FCM) is drawn to indicate the causal relationships between the delay factors and the evaluation factors. For this purpose, the causal relationships between 20 delay factors and four evaluation factors are considered. Afterward, the effect of each factor on management goals is evaluated by using a hybrid learning algorithm. Delay factors are further prioritized by applying fuzzy data envelopment analysis (FDEA). In the second phase, an interpretive structural modeling (ISM) is employed to determine the root causes of delay factors. Findings Results of the first phase show that “supervision technical weaknesses for overcoming technical and executive workshop problems” and “Inaccurate estimation of workload, required equipment and project completion time” are the most significant delay factors. In contrary, “non-use of new engineering contracts” has the lowest impact on the management goals. Meanwhile, the results of the second phase conclude that factors like “Inaccurate estimation of workload, required equipment and project completion time” “weakness of laws and regulations related to job responsibilities” and “lack of foreseen of fines and encouragements in the contracts” are the most significant root factors of delay in construction projects. Originality/value This paper integrates three methods including FCM method, FDEA and ISM. In the first phase, FCM is drawn according to the experts’ opinions and concerning management goals and delay factors. Later, these factors are prioritized according to the results of running the algorithm and using the FDEA model. The second phase, the seven-step in the ISM methodology, is done to identify the root factors. To ensure that the root factors of the delay are at a lower level of hierarchical structure, delay factors are partitioned by drawing the ISM model.

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