A general framework for the manufacturing workstation design optimization: a combined ergonomic and operational approach

Vilas, David del Rio; Longo, Francesco; Monteil, Nadia Rego

doi:10.1177/0037549712462862

Cited by 31 publications

(17 citation statements)

References 53 publications

(56 reference statements)

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“…The design approach can help in improving workers' productivity and in reducing the risk of injury. This design process extends previous studies [20,23], in that it includes the object mass as a variable in the optimisation process and tests a much larger number of designs; it is therefore likely to achieve a better workplace.…”

Section: Discussionmentioning

confidence: 63%

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Automated Simulation-Based Workplace Design that Considers Ergonomics and Productivity

Harari¹,

Bechar²,

Raschke³

et al. 2017

Int. j. simul. model.

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When designing a workplace with manual material handling tasks, it is important to consider both production and ergonomics. We developed an automated workplace design methodology that addresses production and ergonomics for tasks involving a handled mass of up to 23 kg. This process combines optimisation and a Digital Human Modelling (DHM) simulation, which yield the production and ergonomic measures. The task cycle time in current DHM simulations is based on Predetermined Motion Time Systems (PMTS). To address reservations about the time prediction accuracy of PMTS, we developed a new time prediction model that takes the influence of the handled mass into consideration. Our model and optimisation process were evaluated by using a case study of a box conveying workplace design. The time prediction model results did indeed agree with the real mass handling behaviour. Three design approaches (objective functions) were compared: considering only production, only ergonomics and both production and ergonomics. Each approach resulted in a different optimal solution.

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Section: Discussionmentioning

confidence: 63%

“…The limitations of studies that have addressed both production and ergonomic aspects [20][21][22][23][24] lie in the fact that they did not consider all possible solutions. In addition, the handled mass in the simulated tasks was low and constant and the task duration was calculated using PMTS.…”

Section: Introductionmentioning

confidence: 99%

Automated Simulation-Based Workplace Design that Considers Ergonomics and Productivity

Harari¹,

Bechar²,

Raschke³

et al. 2017

Int. j. simul. model.

View full text Add to dashboard Cite

show abstract

“…Grandjean () and Del Rio Vilas, Longo, and Monteil () recommended the work surface height for standing work as 85–90 cm for women and 90–95 cm for men, respectively. On the basis of their research, we created Table and performed experiments with recommended height.…”

Section: Resultsmentioning

confidence: 99%

Development of an Advanced Picking Station Considering Human Factors

2016

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Goods to destination consists of racks, shuttles, conveyors, and picking stations. Racks are used for storing tots, shuttles and conveyors are used for transferring totes, and picking stations are used for order picking. Picking activities are typically repetitive and physically demanding. Awkward postures can result in muscle fatigue and injuries. To understand picking activities and human factors, we performed picking tests and descriptive surveys with participants. Different postures with different picking desk configurations were tested and analyzed with 30 participants. Among the participants, 10 people participated in an electromyograph test and provided subjective results. In case of the LED indicator location of the digital picking station (DPS), 66% of participants felt comfortable when the LED was located between waist level and eye level. In case of the button location of the DPS, 70% of participants felt comfortable when the button was located at waist level. The result shows different views of the DPS application in the warehouse. Last, most participants felt comfortable when totes were placed 5–10 cm higher than the height of the average workstation. This study shows various options for designing picking stations. We believe that this research will provide good guidelines for picking station design which could lead to increases in productivity and workers’ convenience.

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“…Responsiveness, robustness, and resilience (known as "Triple R") become more and more important in logistics and material handling [72] because customer satisfaction is based on "Triple R"-based performance of manufacturing and related logistics operations [73]. The objective functions and constraints are based on the problems of typical material handling related problems, like facility location [74], allocation [75], lot sizing [76][77][78], shortage planning [79], scheduling [80], inventory planning [81], and ergonomic [82] and trade policy aspects [83]. Integrated approaches [52], multi-objective optimization problems [53] can be solved with other effective optimization methods, like teaching-learning based optimization [54], force generated graph algorithms [55], agent-based optimization methods [56], or TOPSIS [57].…”

Section: Content Analysismentioning

confidence: 99%

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

et al. 2019

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In the context of Industry 4.0, the matrix production concept represents revolutionary solutions from a technological and logistics point of view. In a matrix production system, flexible, configurable production and assembly cells are arranged in a grid layout, and the in-plant supply is based on autonomous vehicles. Adaptable and flexible material handling solutions are required to perform the dynamically changing supply-demands of standardized and categorized manufacturing and assembly cells. Within the frame of this paper, the authors describe the in-plant supply process of matrix production and the optimization potential in these processes. After a systematic literature review, this paper introduces the structure of matrix production as a cyber-physical system focusing on logistics aspects. A mathematical model of this in-plant supply process is described including extended and real-time optimization from routing, assignment, and scheduling points of view. The optimization problem described in the model is an NP-hard problem. There are no known efficient analytical methods to find the best solution for this kind of problem; therefore, we use heuristics to find a suitable solution for the above-described problem. Next, a sequential black hole–floral pollination heuristic algorithm is described. The scenario analysis, which focuses on the clustering and routing aspects of supply demands in a matrix production system, validates the model and evaluates its performance to increase cost-efficiency and warrants environmental awareness of the in-plant supply in matrix production.

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A general framework for the manufacturing workstation design optimization: a combined ergonomic and operational approach

Cited by 31 publications

References 53 publications

Automated Simulation-Based Workplace Design that Considers Ergonomics and Productivity

Automated Simulation-Based Workplace Design that Considers Ergonomics and Productivity

Development of an Advanced Picking Station Considering Human Factors

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

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