Design and Development of a Genetic Algorithm Based on Fuzzy Inference Systems for Personnel Assignment Problem

Rabiei, Peyman; Aranda, Daniel Arias

doi:10.4995/wpom.14699

Cited by 3 publications

(3 citation statements)

References 75 publications

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“…Another promising direction for future research entails harnessing advanced technologies to augment the prediction accuracy of machine learning models within the proposed framework. This concept is inspired by the work of [40,41]. In [40], the authors employed Fuzzy Inference Systems (FIS) to tackle a personnel assignment problem, illustrating that FIS can adeptly accommodate the inherent uncertainty and imprecision related to human behavior and subjective evaluations.…”

Section: Discussionmentioning

confidence: 99%

“…This concept is inspired by the work of [40,41]. In [40], the authors employed Fuzzy Inference Systems (FIS) to tackle a personnel assignment problem, illustrating that FIS can adeptly accommodate the inherent uncertainty and imprecision related to human behavior and subjective evaluations. In [41], the authors applied FIS to a disaster relief volunteer management problem, using FIS to encapsulate decision-makers' knowledge and emulate the human reasoning process.…”

Section: Discussionmentioning

confidence: 99%

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Leveraging Machine Learning and Simulation to Advance Disaster Preparedness Assessments through FEMA National Household Survey Data

et al. 2023

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Effective household and individual disaster preparedness can minimize physical harm and property damage during catastrophic events. To assess the risk and vulnerability of affected areas, it is crucial for relief agencies to understand the level of public preparedness. Traditionally, government agencies have employed nationwide random telephone surveys to gauge the public’s attitudes and actions towards disaster preparedness. However, these surveys may lack generalizability in certain affected locations due to low response rates or areas not covered by the survey. To address this issue and enhance the comprehensiveness of disaster preparedness assessments, we develop a framework that seamlessly integrates machine learning and simulation. Our approach leverages machine learning algorithms to establish relationships between public attitudes towards disaster preparedness and demographic characteristics. Using Monte Carlo simulation, we generate datasets that incorporate demographic information of the affected location based on government-provided demographic distribution data. The generated dataset is then input into the machine learning model to predict the disaster preparedness attitudes of the affected population. We demonstrate the effectiveness of our framework by applying it to Miami-Dade County, where it accurately predicts the level of disaster preparedness. With this innovative approach, relief agencies can have a clearer and more comprehensive understanding of public disaster preparedness.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Leveraging Machine Learning and Simulation to Advance Disaster Preparedness Assessments through FEMA National Household Survey Data

et al. 2023

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“…A combination of Fuzzy approaches with genetic algorithms has been proposed in [24] to handle uncertainty in subjective knowledge and evaluate the potential assignment of candidates to job vacancies based on their competency and the significance of each position.…”

Section: A Algorithms and Modelsmentioning

confidence: 99%

Betting on Yourself: A Decision Model for Human Resource Allocation Enriched With Self-Assessment of Soft Skills and Preferences

et al. 2022

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Recently, many approaches were proposed to support human resource management in finding the best human resources for available jobs. However, existing solutions do not effectively evaluate employees' skills, or they do only partially, neither provide mechanisms to describe subjects' skills and desiderata. To face this issue, this paper proposes a decision model for assisting human resource management in effectively evaluating the degree of mutual satisfaction in job-employee assignments. In particular, the decision model has been devised with the following core characteristics: i) employees' skills are modeled by combining hard skills (e.g.: academic training and competencies) and soft skills (e.g.: socio-relational experiences); ii) employees' soft skills are self-evaluated, giving importance not so much to experiences possessed but rather how such skills have been applied over time; iii) employees and managers can selfevaluate their preferences to enable the achievement of the optimal allocation by maximizing the global mutual satisfaction iv) partial matches between characteristics and desires of both employees and jobs are measured through a set of tailored fuzzy metrics. The proposed decision model has been validated in a real case to support the allocation of newly hired employees among open job positions in a Public Administration. Results showed an adequate ability of the proposed model both to support the description of employees, skills, jobs and preferences, and to suggest the best allocation maximizing the global mutual satisfaction. Summarizing, a decision model for human resource management with innovative characteristics is proposed and used to support decisions for a real allocation problem.

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Design System of Urban Residential Environment Based on Interactive Genetic Algorithm

Ding¹,

Qin²

2022

Communications in Computer and Information Science

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Design and Development of a Genetic Algorithm Based on Fuzzy Inference Systems for Personnel Assignment Problem

Cited by 3 publications

References 75 publications

Leveraging Machine Learning and Simulation to Advance Disaster Preparedness Assessments through FEMA National Household Survey Data

Leveraging Machine Learning and Simulation to Advance Disaster Preparedness Assessments through FEMA National Household Survey Data

Betting on Yourself: A Decision Model for Human Resource Allocation Enriched With Self-Assessment of Soft Skills and Preferences

Design System of Urban Residential Environment Based on Interactive Genetic Algorithm

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