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Showing 2 results for Optimization

Ehsanollah Habibi, Mina Salehi, Ali Taheri, Ghasem Yadegarfar,
Volume 5, Issue 4 (3-2018)
Abstract

Background: Recently adaptive neuro-fuzzy inference system is used for the classification of physical load based on three parameters including %HRmax, HRrest, and body weight. The aim of this study was to optimize this model to reduce the error and increase the accuracy of the model in the classification of physical load.
 Methods: The heart rate and oxygen consumption of 30 healthy men were measured during a step test in the laboratory. The VO2max of the participants was measured directly during a maximal treadmill test. A relationship was observed between the calculated %VO2max which is considered as the gold standard of physical load and the model inputs using ANFIS in MATLAB software version 8.0.0. the genetic algorithm was then applied as an optimization technique to the model.
Results: accuracy, sensitivity, and specificity of the model increased after optimization. The average of accuracy accelerated from 92.95% to 97.92%. The RMSE decreased from 5.4186 to 3.1882. Also, in %VO2max estimation, the maximum error of the mode was ±5% after optimization.
Conclusion: The results of this study show that the use of Genetic Algorithm during training process can increase the accuracy and decrease the error of ANFIS model in the estimation of%VO2max. . The advantages of this model include high precision, simplicity and applicability in real-world working environments and also interpersonal differences.

Hamed Salmanzadeh, Arash Mirhaji Moghanjoughi,
Volume 14, Issue 3 (10-2026)
Abstract

Background and Objectives: Low back musculoskeletal disorders are among the major concerns in the construction industry and are primarily caused by biomechanical loads imposed on the lower back. Previous studies on task allocation have mainly focused on reducing maximum physical risk and, in some cases, psychological risk, while ergonomic indices have predominantly been assessed using qualitative or semi-quantitative methods. However, fairness in the distribution of risk and working time among workers has received less attention. Therefore, this study was conducted to develop a model for reducing risk and ensuring its equitable distribution, as well as the equitable distribution of working time. From a macroergonomic perspective, such a model can enhance personnel motivation and prevent tensions arising from perceived inequality in the workplace.

Methods: This cross-sectional study employed a mathematical modeling approach. Postural and force exertion data were collected from four construction workers performing nine distinct tasks. Compressive forces on the lumbar region were determined using 3DSSPP software. Subsequently, the quantitative risk associated with force intensity and exposure duration was computed using the Mainz-Dortmund Dosis Model (MDD), following which a mathematical model was formulated to facilitate the fair assignment of tasks.
Results: Implementation of the developed mixed-integer quadratic programming model revealed that, under divisible task allocation, the cumulative low-back risk and static exposure time per worker were 5,138 newton-hours(Nh) ( below the permissible threshold of 5,500) and 1.575 hours, respectively. Furthermore, the objective function's proximity to zero substantiates the equitable distribution of both risk and working time.
Conclusion: While maintaining operational constraints, the proposed model improves ergonomic equity and can be used as a decision-making tool to protect workers’ lumbar region


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