An Adaptive Network Based Fuzzy Inference System algorithm for assessment and improvement of job security among operators with respect to HSE-Ergonomics program

V. Nadimi, A. Azadeh, M. Rouzbahman, Morteza Saberi, S. Shabibi
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引用次数: 3

Abstract

Researchers have been continuously trying to improve human performance with respect to Health, Safety and Environment (HSE) and ergonomics (hence HSEE). Performance measurement and assessment of operators are fundamental to management planning and control activities, and accordingly, have received considerable attention by both management practitioners and theorists. There has been several efficiency frontier analysis methods reported in the literature. However, each of these methodologies has its strength as well as major limitations. This study proposes a non-parametric efficiency frontier analysis methods based on Adaptive Network-Based Fuzzy Inference System (ANFIS) for measuring efficiency as a complementary tool for performance assessment and improvement of operators. The proposed ANFIS algorithm is able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. The proposed approach is applied to a set of operators in a petrochemical unit to show its applicability and superiority. In fact, this study proposes an adaptive intelligence algorithm for measuring and improving job security among operators with respect to HSE-Ergonomics in a petrochemical unit. To achieve the objectives of this study, standard questionnaires with respect to HSE-Ergonomics are completed by operators. The average results for each category of HSE-Ergonomics are used as inputs and work job security is used as output for the algorithm. Moreover, this algorithm is used to rank operators performance with respect to HSE-Ergonomics. Finally, normal probability technique is used to identify outlier operators. This is the first study that introduces an integrated intelligence algorithm for assessment and improvement of human performance with respect to HSE-Ergonomics program in complex systems.
一种基于自适应网络的模糊推理系统算法,用于hse -工效学项目操作人员工作安全的评估和改进
研究人员一直在不断努力提高人类在健康、安全和环境(HSE)和人体工程学(HSEE)方面的表现。作业人员的绩效测量和评估是管理计划和控制活动的基础,因此受到管理实践者和理论家的相当重视。文献中已经报道了几种效率前沿分析方法。然而,每种方法都有其优点和主要局限性。本研究提出一种基于自适应网络模糊推理系统(ANFIS)的非参数效率前沿分析方法,以测量效率,作为经营者绩效评估和改进的补充工具。所提出的ANFIS算法能够基于一组输入输出观测数据找到随机前沿,并且不需要对随机前沿的功能结构进行明确的假设。此外,它使用与计量经济学方法相似的方法来计算效率分数。以某石化装置的一组操作人员为例,验证了该方法的适用性和优越性。事实上,本研究提出了一种自适应智能算法,用于测量和提高石化单位作业人员在hse -工效学方面的工作安全性。为了达到本研究的目的,操作员完成了hse -人机工程学的标准问卷。每个hse -工效学类别的平均结果被用作输入,工作岗位安全被用作算法的输出。此外,该算法还用于对操作员在hse -工效学方面的表现进行排名。最后,利用正态概率技术识别离群算子。这是第一个引入综合智能算法的研究,用于评估和改进复杂系统中hse -人体工程学程序中的人类表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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