Multi-objective Optimization

Coello, Carlos A. Coello

doi:10.1007/978-3-319-07124-4_17

Cited by 11 publications

(6 citation statements)

References 172 publications

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“…The algorithm proposed in this paper follows the general principles of evolutionary algorithms [41]. A set of structures representing solutions to the problem are evolved performing a stochastic guided search for the best solution.…”

Section: The Rgamentioning

confidence: 99%

Designing Cyclic Job Rotations to Reduce the Exposure to Ergonomics Risk Factors

Diego-Más

2020

IJERPH

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Job rotation is an administrative solution to prevent work-related musculoskeletal disorders that has become widespread. However, job rotation schedules development is a complex problem. This is due to the multi-factorial character of the disorders and to the productive and organizational constraints of the real working environments. To avoid these problems, this work presents an evolutionary algorithm to generate rotation schedules in which a set of workers rotate cyclically over a small number of jobs while reducing the potential for injury. The algorithm is able to generate rotation schedules that optimize multiple ergonomics criteria by clustering the tasks into rotation groups, selecting the workers for each group, and determining the sequence of rotation of the workers to minimize the effects of fatigue. The algorithm reduces prolonged exposure to risks related to musculoskeletal injuries and simplifies the assignment of workers to different tasks in each rotation. The presented procedure can be an effective tool for the design of job-rotation schedules that prevent work-related musculoskeletal disorders while simplifying scheduled changeovers at each rotation and facilitating job monitoring.

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Section: The Rgamentioning

confidence: 99%

Designing Cyclic Job Rotations to Reduce the Exposure to Ergonomics Risk Factors

Diego-Más

2020

IJERPH

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“…The underlying question posed here is: how fast the RTS techniques can successfully fulfil each of these use cases? This question is answered in a qualitative manner using the concept of Pareto optimality [29].…”

Section: Rq3 -Trade-offmentioning

confidence: 99%

Enhancing Genetic Improvement of Software with Regression Test Selection

Guizzo

Petke

Sarro

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI).We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness.To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static RTS techniques with GI to improve seven real-world software programs.The results of our empirical evaluation show that incorporation of RTS within GI significantly speeds up the whole GI process, making it up to 68% faster on our benchmark set, being still able to produce valid software improvements.Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of RTS in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area.

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“…This means, that at a specific optimization step, the improving of one variable results in the worsening of the other objective functions. For that, Coello (2018) describes a wide range of optimization methods that offer opportunities to identify the best trade-off between the competing variables. Especially, the concept of Pareto optimizations show a systematic procedure to find a set of optimal solutions that are superior to additional solutions in the defined parameter space but non-dominated to each other.…”

Section: Post-processing: Generation Of Optimized Clinch Tool Geometriesmentioning

confidence: 99%

Approach for the Automated and Data-Based Design of Mechanical Joints.

2021

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As a result of the increasing challenges in the field of lightweight constructions, the demand for efficient joining technologies is continuously rising. For this purpose, cold forming processes offer an environmental friendly and fast alternative to established joining methods (e.g. welding). However, to ensure a high reliability, not only the selection of an appropriate procedure, but also the dimensioning of the individual joint is essential. While product designers can rely on a wide range of design principles for thermal processes, the dimensioning and evaluation of mechanical joining processes is mainly based on expert knowledge and a few experimental tests. Although few studies already investigated the numerical analysis of mechanical joints, an approach for the sustainable and consistent optimization of the strength and reliability of joining connections for varying use-cases is not available yet. Motivated by this lack, this paper presents an approach for the automated transfer of information within the process chain and the data-based analysis of mechanical joints by using clinching as an example. Therefore, the CRISP-DM reference model is used for the systematic data mining.

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Multi-objective Optimization

Cited by 11 publications

References 172 publications

Designing Cyclic Job Rotations to Reduce the Exposure to Ergonomics Risk Factors

Designing Cyclic Job Rotations to Reduce the Exposure to Ergonomics Risk Factors

Enhancing Genetic Improvement of Software with Regression Test Selection

Approach for the Automated and Data-Based Design of Mechanical Joints.

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