Human-centric optimisation in manual picker-to-parts systems: a holistic energy expenditure approach

Despite the increasing automation trends in current Industry 5.0 scenarios, human operators remain a key resource in manual warehouse operations thanks to their flexibility and cost-effectiveness. Ensuring the well-being of workers in manual picker-to-parts systems remains a crucial challenge. Existing methodologies tend to overlook or inadequately consider the energy expenditure that can be provided by workers during manual picking activities, emphasising the need for a more comprehensive approach. In this work, a novel methodology to quantify the working allowable energy expenditure rate (WAEER) of workers is proposed. The approach considers individual (age, sex, body weight) as well as more holistic features such as lifestyle and non-working daily activities to tailor workloads in executing manual picking tasks. By adopting Mixed Integer Linear Programming (MILP), the proposed approach optimises the assignment of picking items by minimising workers’ fatigue or by avoiding it through rest allowances. Results obtained highlight the effectiveness of the approach as a planning tool, ensuring the workers’ well-being. Future research directions include extending the methodology to hybrid warehouse scenarios or assembly lines. The present article contributes to a forward-thinking framework for human-centric energy-based optimisation within Industry 5.0.

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Publication Details

Journal
International Journal of Production Research
Published
2024-09-28
DOI
https://doi.org/10.1080/00207543.2024.2406023
Citations
11
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
3.84

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article

Human-centric optimisation in manual picker-to-parts systems: a holistic energy expenditure approach

Fabio Sgarbossa, Andrea Lucchese, Salvatore Digiesi
11 citations
International Journal of Production Research
Advanced Manufacturing and Logistics Optimization
3.84
article

Human-centric optimisation in manual picker-to-parts systems: a holistic energy expenditure approach

Fabio Sgarbossa, Andrea Lucchese, Salvatore Digiesi
article en
11 citations

Abstract

Despite the increasing automation trends in current Industry 5.0 scenarios, human operators remain a key resource in manual warehouse operations thanks to their flexibility and cost-effectiveness. Ensuring the well-being of workers in manual picker-to-parts systems remains a crucial challenge. Existing methodologies tend to overlook or inadequately consider the energy expenditure that can be provided by workers during manual picking activities, emphasising the need for a more comprehensive approach. In this work, a novel methodology to quantify the working allowable energy expenditure rate (WAEER) of workers is proposed. The approach considers individual (age, sex, body weight) as well as more holistic features such as lifestyle and non-working daily activities to tailor workloads in executing manual picking tasks. By adopting Mixed Integer Linear Programming (MILP), the proposed approach optimises the assignment of picking items by minimising workers’ fatigue or by avoiding it through rest allowances. Results obtained highlight the effectiveness of the approach as a planning tool, ensuring the workers’ well-being. Future research directions include extending the methodology to hybrid warehouse scenarios or assembly lines. The present article contributes to a forward-thinking framework for human-centric energy-based optimisation within Industry 5.0.

International Journal of Production ResearchVol. 63(7)
Norwegian University of Science and Technology (NO), Polytechnic University of Bari (IT)
Ministero dell’Istruzione, dell’Università e della Ricerca
Affordable and clean energy
Openalex Percentile: Top 6%
Advanced Manufacturing and Logistics Optimization
3.84
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Human-centric optimisation in manual picker-to-parts systems: a holistic energy expenditure approach — Fabio Sgarbossa, Andrea Lucchese, et al. · International Journal of Production Research (2024) | TGRS Research Map | TGRS