Research on the factors of extremely short construction period under the sufficient resources based on Grey-DEMATEL-ISM

Peng, Junlong; Peng, Chao; Wang, Mengyao; Hu, Ke; Wu, Du-bin

doi:10.1371/journal.pone.0265087

Cited by 9 publications

(7 citation statements)

References 64 publications

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“…The main principle of DEMATEL is to build a direct impact matrix and a comprehensive impact matrix by analyzing the causal relationship between various influencing factors in the system, determine the centrality and cause degree, and simplify the complex relationship (Gani et al, 2022). ISM is a structural modeling algorithm that studies the relationship between the influence and the affected of various elements in a complex system, clearly reflects the internal structure of the system, and uses directed graphs and matrices to discover the main factors and their internal relations (Peng et al, 2022). These two algorithms can complement each other.…”

Section: Existing Research Methods For Influencing Factors Of Scrmentioning

confidence: 99%

“…If expert 1 scores x 12 as 3 points, then in the corresponding gray number matrix X1, x 12 = [0.5, 0.75], and note that the interval gray numbers on the diagonal are all [0, 0]. (2) Based on the comprehensive consideration of the different research fields and research levels of experts, the different degrees of mastery of the research fields, reference resources (Peng et al, 2022) gives each expert fuzzy interval gray weight according to the importance of experts, and the weight semantic scale is shown in Use formula (1) to calculate the upper and lower limits of the standardized gray number.…”

Section: Establishment Of Gray Direct Relation Matrixmentioning

confidence: 99%

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Influencing factors on green supply chain resilience of agricultural products: an improved gray-DEMATEL-ISM approach

Wang,

Liu,

Chen

et al. 2023

Front. Sustain. Food Syst.

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IntroductionNatural disasters and the COVID-19 epidemic have caused serious consequences such as long-term disruption and chain reaction to the global supply chain. Global warming caused by a large number of greenhouse gases has accelerated the attention of society to environmental sustainability. Therefore, identifying the transmission path of the factors that affect the green supply chain resilience of agricultural products is the primary task to accelerate the construction of a modern circulation system of agricultural products, ensure market supply and protect the environment.MethodsBased on the stakeholder theory, this study uses the literature research method to identify 15 factors that affect the green supply chain resilience of agricultural products. Through improving DEMATEL and ISM to study the internal relationship between the influencing factors, build a multi-level hierarchical structure model, and identify the basic transmission process and path of the influencing factors.ResultsThe results show that the government’s issuance of environmental policies and the provision of financial subsidies are important driving forces to strengthen the green supply chain resilience of agricultural products; The collaboration capability and business sustainability goals directly affect the green supply chain resilience of agricultural products; Agility, digital infrastructure construction, sustainability beliefs of top managers, public opinion with environment information disclosure and other factors indirectly affect the green supply chain resilience of agricultural products.DiscussionThe conclusion shows that the most important way to guide the green supply chain of agricultural products to develop towards standardization, normalization and sustainability is to guide the organization to set business sustainable goals and strengthen the collaborative cooperation ability of all stakeholders in the supply chain, with the government issuing environmental policies and providing financial subsidies as the driving factors. This study can provide theoretical basis for the government and enterprises to strengthen the green supply chain resilience of agricultural products.

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Section: Existing Research Methods For Influencing Factors Of Scrmentioning

confidence: 99%

Section: Establishment Of Gray Direct Relation Matrixmentioning

confidence: 99%

Influencing factors on green supply chain resilience of agricultural products: an improved gray-DEMATEL-ISM approach

Wang,

Liu,

Chen

et al. 2023

Front. Sustain. Food Syst.

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“…In addition, the choice of forecasting tools, the effect of cognitive uncertainty, contractor management capability, frequent design changes and initial over-optimism on the part of the owner can also affect construction duration (Ahmadu et al, 2020;Velumani et al, 2021;Doloi et al, 2012). Understanding the interrelationships and the degree of influence of the factors affecting the duration can help decision makers and managers of construction projects to effectively master the key factors with sufficient resources (Peng et al, 2022). However, not every factor has an important influence on the construction period, so before establishing the prediction model, it is necessary to select specific factors affecting the construction period as the input of the model, and get the output results after the model processing.…”

Section: Selection Of Indicatorsmentioning

confidence: 99%

Research on residential building duration prediction model based on mean clustering and neural network

Ji,

Nan,

Wei

et al. 2023

Preprint

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The duration target will directly affect the effectiveness of the implementation of the residential building project, so it is necessary to predict a reasonable residential building duration. In this work, genetic algorithm (GA) is used to optimize and improve the weights and thresholds of the BP neural network to form a GA-BP neural network model, and seven parameters, such as aboveground building structure, number of building floors, building area, decoration standards, foundation structure, number of underground building floors, and underground building area, are taken as the input parameters of the neural network model, and the residential building duration is taken as the output parameter. 111 sets of residential building duration data were collected and divided into 90 training sets and 21 test sets, and the model was validated and analyzed in comparison using root mean square error (RMSE), correlation coefficient (R) and average error rate, which were used to validate that the GA-BP neural network model helps in predicting the duration of residential buildings. In order to improve the prediction accuracy of GA-BP neural network model, this work uses artificial bee colony improved K-means clustering algorithm to classify 111 sets of experimental data and 33 sets of new data, according to the classification of the training and testing results show that the ABC-K-means-GA-B model all have a strong generalization ability and strong prediction accuracy, and at the same time, it proves that this paper's proposed ABC- K-means-GA-BP neural network helps to predict the construction period of residential buildings, which is of great practical significance for improving construction efficiency.

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“…Therefore, even under the condition of sufficient resources, there is still a problem of rational allocation of resources. Peng et al (2022) studied the factors affecting the extremely short construction period under the condition of the sufficient resources, and investigated the effective key factors that promote the success of the project. The success of construction largely projects depends on the appropriate management of human resources (Dabirian et al, 2019).…”

Section: Review Of Literaturementioning

confidence: 99%

Labor resource allocation under extremely short construction period based on the inverse optimization method

Peng

Liu

2022

ECAM

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PurposeThis research is aimed to mainly be applicable to expediting engineering projects, uses the method of inverse optimization and the double-layer nested genetic algorithm combined with nonlinear programming algorithm, study how to schedule the number of labor in each process at the minimum cost to achieve an extremely short construction period goal.Design/methodology/approachThe method of inverse optimization is mainly used in this study. In the first phase, establish a positive optimization model, according to the existing labor constraints, aiming at the shortest construction period. In the second phase, under the condition that the expected shortest construction period is known, on the basis of the positive optimization model, the inverse optimization method is used to establish the inverse optimization model aiming at the minimum change of the number of workers, and finally the optimal labor allocation scheme that meets the conditions is obtained. Finally, use algorithm to solve and prove with a case.FindingsThe case study shows that this method can effectively achieve the extremely short duration goal of the engineering project at the minimum cost, and provide the basis for the decision-making of the engineering project.Originality/valueThe contribution of this paper to the existing knowledge is to carry out a preliminary study on the relatively blank field of the current engineering project with a very short construction period, and provide a path for the vast number of engineering projects with strict requirements on the construction period to achieve a very short construction period, and apply the inverse optimization method to the engineering field. Furthermore, a double-nested genetic algorithm and nonlinear programming algorithm are designed. It can effectively solve various optimization problems.

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Research on the factors of extremely short construction period under the sufficient resources based on Grey-DEMATEL-ISM

Cited by 9 publications

References 64 publications

Influencing factors on green supply chain resilience of agricultural products: an improved gray-DEMATEL-ISM approach

Influencing factors on green supply chain resilience of agricultural products: an improved gray-DEMATEL-ISM approach

Research on residential building duration prediction model based on mean clustering and neural network

Labor resource allocation under extremely short construction period based on the inverse optimization method

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