Optimization of Productivity and Worker Well-Being by Using a Multi-Objective Optimization Framework

Pascual, Aitor Iriondo; Högberg, Dan; Lämkull, Dan; Luque, Estela Perez; Syberfeldt, Anna; Hanson, Lars

doi:10.1080/24725838.2021.1997834

Cited by 11 publications

(7 citation statements)

References 37 publications

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“…We used the database of a multi-objective optimization of worker well-being and productivity application study of a welding gun workstation [20] to apply knowledge discovery methods to obtain knowledge for future workstation designs.…”

Section: Methodsmentioning

confidence: 99%

“…We have used data mining to find decision rules that can extract such knowledge from optimization datasets of multi-objective optimizations of worker well-being and productivity. The database generated in a previous multi-objective optimization application study [20] was mined to find rules that can be applied to similar workstations. The rules were created with the intention of balancing worker well-being and productivity, taking into account the anthropometric diversity of workers, so as to find critical solutions that can distinguish specific populations.…”

Section: Introductionmentioning

confidence: 99%

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Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity

et al. 2022

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Usually, optimizing productivity and optimizing worker well-being are separate tasks performed by engineers with different roles and goals using different tools. This results in a silo effect which can lead to a slow development process and suboptimal solutions, with one of the objectives, either productivity or worker well-being, being given precedence. Moreover, studies often focus on finding the best solutions for a particular use case, and once solutions have been identified and one has been implemented, the engineers move on to analyzing the next use case. However, the knowledge obtained from previous use cases could be used to find rules of thumb for similar use cases without needing to perform new optimizations. In this study, we employed the use of data mining methods to obtain knowledge from a real-world optimization dataset of multi-objective optimizations of worker well-being and productivity with the aim to identify actionable insights for the current and future optimization cases. Using different analysis and data mining methods on the database revealed rules, as well as the relative importance of the design variables of a workstation. The generated rules have been used to identify measures to improve the welding gun workstation design.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity

et al. 2022

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“…In order to utilize the NSGA-II algorithm, an initial population of candidate solutions (parents) is required, referred to as population zero, P(0). Based on the number of decision variables (11) and the recommendation/rule of thumb to use 10 times the decision variables [21], a population size of 110 individuals was determined. These 110 individuals were created by random sampling via the developed PY code.…”

Section: Population Zero P(0)mentioning

confidence: 99%

“…Positive attempts using multi-objective methods in manufacturing planning processes have been carried out, e.g., [11], which presents an industrial demonstrator focusing on optimizing a welding station process. However, that work does not consider changing positions of the equipment setup, meaning the layout planning itself is not optimized.…”

Section: Introductionmentioning

confidence: 99%

Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory

Lind

Elango

Hanson

et al. 2023

Systems

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The planning and design process of manufacturing factory layouts is commonly performed using digital tools, enabling engineers to define and test proposals in virtual environments before implementing them physically. However, this approach often relies on the experience of the engineers involved and input from various cross-disciplinary functions, leading to a time-consuming and subjective process with a high risk of human error. To address these challenges, new tools and methods are needed. The Industry 5.0 initiative aims to further automate and assist human tasks, reinforcing the human-centric perspective when making decisions that influence production environments and working conditions. This includes improving the layout planning process by making it more objective, efficient, and capable of considering multiple objectives simultaneously. This research presents a demonstrator solution for layout planning using digital support, incorporating a virtual multi-objective optimization approach to consider safety regulations, area boundaries, workers’ well-being, and walking distance. The demonstrator provides a cross-disciplinary and transparent approach to layout planning for an assembly station in the context of battery production. The demonstrator solution illustrates how layout planning can become a cross-disciplinary and transparent activity while being automated to a higher degree, providing results that support decision-making and balance cross-disciplinary requirements.

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“…This software can also be implemented in proactive ergonomics, in the design of test procedures for the interior of the vehicle design process [ 4 ]. The software can also be applied as a standard for the assessment of the well-being of workers via the RULA score, as described in [ 5 ]. The implementation of software solutions for ergonomic load assessment of workers was also discussed by a pair of authors, Grobelny and Michalski [ 6 ], who digitized the workplace and workers using Anthropos ErgoMAX software, to devise a workplace design methodology that prevents work-related musculoskeletal disorders (WMSDs).…”

Section: Introductionmentioning

confidence: 99%

Ergonomic Rationalization Sequence of Digital Lighting Design in the Working Environment

Dupláková

Duplák

Kascak

2022

IJERPH

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This paper describes the creation of a rationalization sequence for working with simulation models, and its subsequent verification in the redesigning of lighting in cooperation with digital enterprise tools. The rationalization sequence consists of 11 sequences, whose accuracy is subsequently verified on a practical example of the redesign of an assembly room of a production hall in the simulation tool Dialux Evo. In conjunction with the proposed procedure, a redesign of the assembly workplace located in the production hall for daylighting, artificial, and mixed lighting was created. The conclusion of the paper provides an overview of the benefits gained from the application of the work environment rationalization procedure.

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Optimization of Productivity and Worker Well-Being by Using a Multi-Objective Optimization Framework

Cited by 11 publications

References 37 publications

Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity

Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity

Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory

Ergonomic Rationalization Sequence of Digital Lighting Design in the Working Environment

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