An approach to develop an effective job rotation schedule by using genetic algorithm

Mondal, Pritom Kumar; Ahsan, A.M.M. Nazmul; Quayum, Kismot Abdul

doi:10.1109/eict.2014.6777883

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…In [4] the proposed model adds posture diversity to reduce the accumulated risk of body postures. Additionally, a study (2014) with the purpose to reduce repetitive task sequences based on ergonomic, environmental, competence and skill factors was found [8].…”

Section: Literature Revisionmentioning

confidence: 99%

A Genetic Algorithm to Design Job Rotation Schedules with Low Risk Exposure

Rodrigues¹,

Gamboa²,

Mollaei³

et al. 2020

IFIP Advances in Information and Communication Technology

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In automotive industries, the manufacturing processes are characterized by repetitive tasks and physically demanding work, with possible long-term implications on the musculoskeletal health of the workers. One key organizational strategy that provides an improvement in the prevention of musculoskeletal disorders are job rotation schedules. These are usually designed manually, being (1) time demanding and (2) a subjective evaluation of the schedule's risk. In this work, a genetic algorithm is presented, to generate automatically a daily job rotation schedule. The quality of the schedule is based on objective scores as-signed to workstations by the European Assembly Worksheet (EAWS) risk screening tool, guiding the algorithm in reaching a final solution that promotes schedules with lower exposure to the sequence of workstations assigned to each worker of the team. The schedules generated by the algorithm were compared to schedules designed by the team leaders and presented a better overall result.

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Section: Literature Revisionmentioning

confidence: 99%

A Genetic Algorithm to Design Job Rotation Schedules with Low Risk Exposure

Rodrigues¹,

Gamboa²,

Mollaei³

et al. 2020

IFIP Advances in Information and Communication Technology

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show abstract

“…Additionally, there is slightly increment in average response time with respect to increase in workload λ. On the whole, LJF scheduling algorithm performs worst not only alone but with combined FCFS as well [12][13][14][15][16][17].…”

Section: Conclusion and Future Scopementioning

confidence: 99%

A New Approach for Job Scheduling Using Hybrid GA-ST Optimization

Monga¹

2017

RDMS

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In the proposed method, the GA-ST has applied over the number of processes regardless of their size as well as priority. Thus, the scheduling is done without considering their types and performs accordingly. The performance of proposed and traditional techniques is compared over different workloads. This work is to maximize the efficiency of the system with the discovery of efficient operating scenario that can handle the load with effective response time. The experimental results are performed using MATLAB software under different mean arrival time i.e. 24, 26 and 28. From the analysis it confirms that proposed technique of scheduling outperforms the other traditional scheduling algorithms and performs expertly in varying workloads.

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“…Existing job rotation programs are focused primarily on managing biomechanical and organizational risks [6]. Analytical models focused on integrating these two perspectives include factors such as details of work contracts (work shifts, part-time, flexible scheduling), worker preferences, level of production, skills, training, physical capacity and experience, as well as the effects of learning [13,[21][22][23][24], exposure to noise, physical workload or anthropometric data [13,22,23,[25][26][27][28][29], age [11,14] and cognitive ergonomic factors such as human reliability or task complexity [27,30,31].…”

Section: Literaturementioning

confidence: 99%

“…• Integrating variables from work sciences into existing process scheduling or balancing models via constraints in the model [21,32]; • Treating the staff allocation problem as a multi-criteria/objective decision problem [21,24]; • Building a model based on heuristics or meta-heuristics (artificial intelligence approaches) such as genetic algorithms, the simulated annealing algorithm or ant algorithm [22,25,28] fuzzy logic [27] or particle swarms [26] to integrate certain operational risks into risks identified in work science [6]; • Building a model based on integer (linear or nonlinear)…”

Section: Literaturementioning

confidence: 99%

Towards Dynamic and Adaptive Allocation of Staff in a Digital-organized Production Context: An Innovative Perspective from Work Science

Nadeau¹,

Landau²

2017

J Ergonomics

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The increasingly complex socio-technical systems in modern businesses are forcing us to adapt our risk management models to a context of digital-organized production. This new phase of industrial digitization is bringing new opportunities and new challenges for managers and workers. A new work organization is emerging, along with new models for optimizing the allocation of labor to production activities. Three approaches are proposed in the literature: Mathematical optimization, models from work sciences, and integration of these two. Integrated analytical models are limited both by the number of variables considered and knowledge of the associated uncertainties. These models are static, not always focused on reducing risks at the source, and based on expert elicitation by unspecified protocols. Advances in this field are raising several questions regarding ethics, legality and safety. A model that allows systematic reduction of occupational health and safety risks and dynamic integration of operational considerations is needed. Successful businesses in the future will rely on managerial approaches that are interdisciplinary, focused on human wellbeing and draw on smart technologies and artificial intelligence.

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An approach to develop an effective job rotation schedule by using genetic algorithm

Cited by 4 publications

References 7 publications

A Genetic Algorithm to Design Job Rotation Schedules with Low Risk Exposure

A Genetic Algorithm to Design Job Rotation Schedules with Low Risk Exposure

A New Approach for Job Scheduling Using Hybrid GA-ST Optimization

Towards Dynamic and Adaptive Allocation of Staff in a Digital-organized Production Context: An Innovative Perspective from Work Science

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