An adaptive algorithm for performance assessment of construction project management with respect to resilience engineering and job security

Hashemi, P.; Yazdanparast, Reza; Ghavamifar, Ali; Azadeh, A.

doi:10.5267/j.jpm.2017.10.002

Cited by 4 publications

(3 citation statements)

References 30 publications

(27 reference statements)

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“…In addition, resilience management and job security exerted similar effects on the total efficiency of the system. 19 In another study, Omidvar et al presented a model for assessing the performance of an organization based on RE using a fuzzy AHP in the petrochemical industry. They presented management commitment and preparedness in the face of emergency conditions and used these main two factors to determine the resilience level.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of resilience engineering using super decisions software

Shirali

Nematpour

2019

Health Promot Perspect

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Background: Resilience engineering (RE) is a new approach to upgrade safety management systems. Due to its novelty in the field of safety, RE seems to be promising in providing good indicators to assess priorities in organizational strengths/weaknesses while planning to promote safety within organizations. Several methods have been recently developed to evaluate RE performance. The current study is an attempt to quantify and determine the priorities of RE dimensions in a steel industry using analysis network process (ANP). Methods: In this cross-sectional study, 489 male workers of a steel industry participated. For this purpose, the RE questionnaire was distributed among workers and, then, super decisions software (version 3.2) was used to analyze the data. Results: The results indicated that there was a sufficient level of RE in the organization where top management commitment with normalized weight 0.1781 and awareness-opacity with normalized weight 0.1483 were ranked as the first and last priorities of the organization, respectively. Conclusion: The results of this study showed that the top management system, with the adoption of safety policies, has been able to improve the performance of RE in the organization. Managers should consider appropriate measures to improve the RE situation.

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Section: Introductionmentioning

confidence: 99%

Evaluation of resilience engineering using super decisions software

Shirali

Nematpour

2019

Health Promot Perspect

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“…For example Modified Memetic Particle Swarm Optimization, Grey Wolf Optimization Algorithm, Simulated Annealing Algorithm and Genetic Algorithm etc. (Chawla et al, 2018a(Chawla et al, , 2018b(Chawla et al, , 2018cHartmann, 1998, Pich et al, 2002Sadjadi et al, 2009;Sadjadi & Sadi-Nezhad, 2017;Moghadam et al, 2012;Sadi-Nezhad, 2017;Hafezalkotob, 2018;Hashemi et al, 2018). The evaluation of sustainable project management should not be limited to only planning and design stage.…”

Section: Fig 1 Critical Parameters To Gauge Sustainability In the Pmentioning

confidence: 99%

The sustainable project management: A review and future possibilities

Chawla¹,

Chanda²,

Angra³

et al. 2018

10.5267/j.jpm

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Sustainability in project operations such as financial, social and environmental sustainability is one of the most prominent issues of the present times to address. The increased focus on sustainable business operations has changed the viewpoint of researchers and corporate community towards the project management. Today sustainability in business operations along with sustainability of natural and environmental resources are of paramount significance which has further caused a huge impact on conception, planning, scheduling and execution of the project management activities. In this paper, a literature review between 1987 and 2018 on different issues affecting the sustainability in project management is carried out. The present study also identifies and discusses the future possibilities to apply computational procedures in order to estimate and optimize the sustainability issues in the management of projects, for example the computational evolutionary algorithms can be applied to formulate the multi-objective decisionmaking problem after considering critical factors of sustainability in the projects and then yielding optimized solutions for the formulated problem to achieve sustainability in the projects. A new integrated framework with the inclusion of feedback function for assessment of each decision and actions taken towards the sustainability of the projects is also identified and presented.

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“…Hashemi, Yazdanparast et al, 2018) proposed an adaptive algorithm to assess the performance of the project management considering resilience engineering and job satisfaction in a large construction site. They distributed a questionnaire on a construction site and analyzed the results using a radial basis function (RBF), arti cial neural networks (ANN-MLP), and statistical data.…”

mentioning

confidence: 99%

Analyzing the Impact of HSEE, Internet of Things, and Resilience Engineering on Job Satisfaction (Case Study: Tehran Electricity Company)

Akbari

Rezaeenour

Tabassian

et al. 2023

Preprint

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Job satisfaction is a significant matter for managers and employees. Job satisfaction denotes the interest of individuals in fulfilling job responsibilities. Job satisfaction in employees increases profitability in the company, loyalty to the company, and work efficiency. Promotion, reciprocal respect, job security, salary, benefits, etc. are some factors increasing job satisfaction in employees. One of the items that affect job satisfaction is the Internet of Things (IoT), which improves the work environment in the organization. Further, resilience engineering (RE) improves the ability of an organization to tackle hazards and threats. In addition to the aforementioned, HSEE has proven significant in organizations and it is beneficial in increasing efficiency. Considering the potential hazards in organizations, focusing on the actions and decisions of employees of organizations is crucial. Therefore, it is essential to analyze health, safety, environment, and ergonomics (HSEE) in the workplace. In this study, a neural network (AI) and an adaptive network-based inference system (ANFIS) have been suggested to analyze the effects of IoT, HSEE, and RE on the job satisfaction of the employees of Tehran Electricity Company. For better training of the recommended networks, a metaheuristic genetic algorithm has been utilized to choose a model with the lowest error. Therefore, a standard questionnaire with IoT, HSEE, and RE as input and job satisfaction as output was prepared. The efficiency of the questionnaire was examined using Cronbach’s alpha. The questionnaire was then distributed among the employees of Tehran Electricity Company. The suggested network was solved in different modes, and the network with the lowest error was selected and combined with metaheuristic algorithms for better training. Lastly, employees with low efficiency were identified, and several solutions were recommended to improve the Maintenance Sector.

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An adaptive algorithm for performance assessment of construction project management with respect to resilience engineering and job security

Cited by 4 publications

References 30 publications

Evaluation of resilience engineering using super decisions software

Evaluation of resilience engineering using super decisions software

The sustainable project management: A review and future possibilities

Analyzing the Impact of HSEE, Internet of Things, and Resilience Engineering on Job Satisfaction (Case Study: Tehran Electricity Company)

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