Mustafa Jahangoshai Rezaee, Samuel Yousefi and Ripon K. Chakrabortty
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…
Abstract
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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Hadi Shabanpour, Saeed Yousefi and Reza Farzipoor Saen
The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical…
Abstract
Purpose
The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.
Design/methodology/approach
It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.
Findings
A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.
Originality/value
We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.
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Peyman Aghdasi, Shayesteh Yousefi and Reza Ansari
In this paper, based on the density functional theory (DFT) and finite element method (FEM), the elastic, buckling and vibrational behaviors of the monolayer bismuthene are…
Abstract
Purpose
In this paper, based on the density functional theory (DFT) and finite element method (FEM), the elastic, buckling and vibrational behaviors of the monolayer bismuthene are studied.
Design/methodology/approach
The computed elastic properties based on DFT are used to develop a finite element (FE) model for the monolayer bismuthene in which the Bi-Bi bonds are simulated by beam elements. Furthermore, mass elements are used to model the Bi atoms. The developed FE model is used to compute Young's modulus of monolayer bismuthene. The model is then used to evaluate the buckling force and fundamental natural frequency of the monolayer bismuthene with different geometrical parameters.
Findings
Comparing the results of the FEM and DFT, it is shown that the proposed model can predict Young's modulus of the monolayer bismuthene with an acceptable accuracy. It is also shown that the influence of the vertical side length on the fundamental natural frequency of the monolayer bismuthene is not significant. However, vibrational characteristics of the bismuthene are significantly affected by the horizontal side length.
Originality/value
DFT and FEM are used to study the elastic, vibrational and buckling properties of the monolayer bismuthene. The developed model can be used to predict Young's modulus of the monolayer bismuthene accurately. Effect of the vertical side length on the fundamental natural frequency is negligible. However, vibrational characteristics are significantly affected by the horizontal side length.
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Mohammad Reza Fathi, Hamid Rahimi and Mehrzad Minouei
The main purpose of this paper is to predicate financial distress using the worst-practice-frontier data envelopment analysis (WPF-DEA) model and artificial neural network.
Abstract
Purpose
The main purpose of this paper is to predicate financial distress using the worst-practice-frontier data envelopment analysis (WPF-DEA) model and artificial neural network.
Design/methodology/approach
In this study, a neural network technique was used to forecast inputs and outputs in the future time-period. Using a WPF-DEA model, financially distressed companies were identified based on the worst performance, and an improvement solution was provided for those decision-making units.
Findings
This study’s findings show that dynamic WPF-DEA has high predictability in corporate financial distress, and it can be used with high confidence. Based on the future time-period results, JOUSH & OXYGEN was predicted to be a financially distressed company in the two future time-periods.
Originality/value
In recent decades, globalization, technological changes and a competitive space have increased uncertainty in the economic environment. In such circumstances, economic growth certainly depends on correct decision-making and optimal allocation of resources. It can be done by introducing appropriate tools and models for assessing corporate financial conditions, including financial distress and bankruptcy.
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Ali Yousefi, Saeed Amir Aslanzadeh and Jafar Akbari
The purpose of this paper is to investigate the surface properties, particle sizes and corrosion inhibition performance of sodium dodecyl sulfate (SDS) in the presence of…
Abstract
Purpose
The purpose of this paper is to investigate the surface properties, particle sizes and corrosion inhibition performance of sodium dodecyl sulfate (SDS) in the presence of imidazolium-based ionic liquid as an additive. Up to now, different properties of alone surfactants and ionic liquids have been studied. However, few studies have been devoted to mixed ionic liquid and surfactant. The significance and novelty of this research is the investigation of 1-methylimidazolium trinitrophenoxide ([MIm][TNP]) as ionic liquid effects on SDS corrosion behavior.
Design/methodology/approach
The inhibition effect of [MIm][TNP], SDS and their mixtures on mild steel surface in 2 M hydrochloric acid (HCl) solution was examined by electrochemical impedance spectroscopy, potentiodynamic polarization (PDP), scanning electron microscopy (SEM), atomic force microscopy and quantum chemical calculations as well as dynamic light scattering (DLS) and surface tension measurements to discuss surface properties of studied solutions.
Findings
Based on the results, ionic liquid/SDS mixtures significantly indicated better inhibition properties than pure surfactant solution. PDP curves indicated that the studied compounds act as mixed-type of inhibitors. The critical micelle concentration, surface properties and particle sizes were investigated from the surface tension measurements and DLS results.
Originality/value
Adsorption of the inhibitors on the steel surface obeyed the Villamil adsorption model. SEM was used for surface analysis and verified the inhibition efficiency of mixed IL/SDS system. Quantum chemical calculations were performed using density functional theory, and a good relationship between experimental and theoretical data has been obtained.
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Rila Anggraeni and Christin Susilowati
Many companies produce environmentally friendly goods and offer their products with varied attractive marketing mix strategies. One of the company's potential target markets is…
Abstract
Many companies produce environmentally friendly goods and offer their products with varied attractive marketing mix strategies. One of the company's potential target markets is millennials because the growing number of this community has become enormous. In terms of behavior, millennials have a high level of consumption compared to other generations. However, there are big questions about the willingness of millennials to consume green products. This study aims to acknowledge the green product buying behavior among millennials, especially premium green products. The variables expected to influence the millennial's willingness to pay premium include environmental concern, reference group, and pro-environmental attitude. Data collected through a survey of 250 respondents. The hypothesis framework was tested using PLS-SEM modeling to evaluate the measurement and structural models with the assistance of Warp PLS version 7.0. This study found that millennials who consider the importance of preserving the environment and have a reference group that solicitude to the environment will have a pro-environmental attitude and willing to buy the green product, even though it has a higher price. Green product's management can use the result to formulate an effective green marketing strategy to target the millennials. Regarding the need for millennials' environmental behavior clearer picture in a developing economy, the present study inflicts the literature by describing the antecedents of millennials' willingness to pay premium green products. The results also give practical implications by shedding light on millennials’ green behavior variables. It helps green entrepreneurs conceive their strategic marketing management, and thus can boost the green economy and economic growth.
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Zsolt Tibor Kosztyán, Tibor Csizmadia, Zoltán Kovács and István Mihálcz
The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk…
Abstract
Purpose
The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk evaluation and to enable effectively integrating the elements of risk evaluation.
Design/methodology/approach
A real case study of an electric motor manufacturing company is presented to illustrate the advantages of this new framework compared to the traditional and fuzzy failure mode and effect analysis (FMEA) approaches.
Findings
The essence of the proposed total risk evaluation framework (TREF) is its flexible approach that enables the effective integration of firms’ individual requirements by developing tailor-made organizational risk evaluation.
Originality/value
Increasing product/service complexity has led to increasingly complex yet unique organizational operations; as a result, their risk evaluation is a very challenging task. Distinct structures, characteristics and processes within and between organizations require a flexible yet robust approach of evaluating risks efficiently. Most recent risk evaluation approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the unique organizational demands and contextual factors. To address this challenge effectively, taking a crucial step toward customization of risk evaluation.
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Zeping Wang, Hengte Du, Liangyan Tao and Saad Ahmed Javed
The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less…
Abstract
Purpose
The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).
Design/methodology/approach
This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.
Findings
The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.
Originality/value
The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.
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Mohammad Heydari, Ghasem Barid Loghmani and Abdul-Majid Wazwaz
The main purpose of this paper is to implement the piecewise spectral-variational iteration method (PSVIM) to solve the nonlinear Lane-Emden equations arising in mathematical…
Abstract
Purpose
The main purpose of this paper is to implement the piecewise spectral-variational iteration method (PSVIM) to solve the nonlinear Lane-Emden equations arising in mathematical physics and astrophysics.
Design/methodology/approach
This method is based on a combination of Chebyshev interpolation and standard variational iteration method (VIM) and matching it to a sequence of subintervals. Unlike the spectral method and the VIM, the proposed PSVIM does not require the solution of any linear or nonlinear system of equations and analytical integration.
Findings
Some well-known classes of Lane-Emden type equations are solved as examples to demonstrate the accuracy and easy implementation of this technique.
Originality/value
In this paper, a new and efficient technique is proposed to solve the nonlinear Lane-Emden equations. The proposed method overcomes the difficulties arising in calculating complicated and time-consuming integrals and terms that are not needed in the standard VIM.
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Ayodeji E. Oke, Seyi S. Stephen and Clinton O. Aigbavboa