A genetic algorithm approach to design job rotation schedules ensuring homogeneity and diversity of exposure in the automotive industry

Assunção, Ana; Mollaei, Nafiseh; Rodrigues, João; Fujão, Carlos; Osório, Daniel; Veloso, António; Gambôa, Hugo; Carnide, Filomena

doi:10.1016/j.heliyon.2022.e09396

Cited by 6 publications

(2 citation statements)

References 25 publications

(48 reference statements)

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“…The cost of intracellular and intercellular movement (TC), system utilization (SU), and system flexibility (SF). These three performance measures are computed using Eqs (10), ( 14) and (19), respectively [45][46][47][48]:…”

Section: Measure Of Performancementioning

confidence: 99%

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Dhayef,

Al-Zubaidi,

Al-Kindi

et al. 2024

PLoS ONE

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Cell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.

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Section: Measure Of Performancementioning

confidence: 99%

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Dhayef,

Al-Zubaidi,

Al-Kindi

et al. 2024

PLoS ONE

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show abstract

“…Bernardes et al [ 5 ] and Assunção et al [ 6 , 7 ] declared that the process of industrial ergonomics has a significant role in the decision-making process in this automotive industry where this study took place, as they are also responsible for risk assessment for occupational biomechanical overload risk factors. Given the worker’s health condition, the matching method entails/implies Occupational Physicians (OP) assigning each worker an authorized exposure to occupational risk factors.…”

Section: Introductionmentioning

confidence: 99%

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

Mollaei

Fujão²,

Silva

et al. 2022

IJERPH

Self Cite

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In automotive and industrial settings, occupational physicians are responsible for monitoring workers’ health protection profiles. Workers’ Functional Work Ability (FWA) status is used to create Occupational Health Protection Profiles (OHPP). This is a novel longitudinal study in comparison with previous research that has predominantly relied on the causality and explainability of human-understandable models for industrial technical teams like ergonomists. The application of artificial intelligence can support the decision-making to go from a worker’s Functional Work Ability to explanations by integrating explainability into medical (restriction) and support in contexts of individual, work-related, and organizational risk conditions. A sample of 7857 for the prognosis part of OHPP based on Functional Work Ability in the Portuguese language in the automotive industry was taken from 2019 to 2021. The most suitable regression models to predict the next medical appointment for the workers’ body parts protection were the models based on CatBoost regression, with an RMSLE of 0.84 and 1.23 weeks (mean error), respectively. CatBoost algorithm is also used to predict the next body part severity of OHPP. This information can help our understanding of potential risk factors for OHPP and identify warning signs of the early stages of musculoskeletal symptoms and work-related absenteeism.

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A Steady-State Genetic Algorithm for the Single Machine Scheduling Problem with Periodic Machine Availability

Chaubey,

Sundar

2023

SN COMPUT. SCI.

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A genetic algorithm approach to design job rotation schedules ensuring homogeneity and diversity of exposure in the automotive industry

Cited by 6 publications

References 25 publications

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

A Steady-State Genetic Algorithm for the Single Machine Scheduling Problem with Periodic Machine Availability

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