Adaptive Storage Reassignment in Order Picking Systems to Picker Learning and Change of Demand

Ogasawara, Ayumi; Ishigaki, Aya; Yasui, Seiichi

doi:10.1016/j.promfg.2020.01.279

Cited by 2 publications

(2 citation statements)

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“…The authors highlight the indispensable need to consider learning and forgetting in the decision-making process, rather than a sole dogmatic focus on travel distance. Despite the rigorousness of the study by Grosse, Glock, and Jaber (2013), some of their assumptions were challenged by Ogasawara, Ishigaki, and Yasui (2019). Especially, they claim it is impractical to presume that all picker learning is reset after a reassignment in the storage area.…”

Section: Dynamic Varying Work Rates In Op Literaturementioning

confidence: 99%

“…Research papers address this phenomenon by including behavioural probabilities in their models. For example, Ogasawara, Ishigaki, and Yasui (2019) use parameters that represent the probability that a picker accesses a wrong shelf area, whereas include route deviation probabilities. Both studies show the importance of integrating stochastic worker behaviour.…”

Section: Stochastic Worker Behaviour and Work Execution In Op Literaturementioning

confidence: 99%

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In pursuit of humanised order picking planning: methodological review, literature classification and input from practice

Lombaert

Braekers

Koster

et al. 2022

International Journal of Production Research

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At the core of every high-performing warehouse is an efficient order picking (OP) system. To attain such a system, policy choices should be carefully aligned with subjects responsible for the actual picking within the established system. Despite recent advancements in automating the picking process due to Industry 4.0, human operators will continue to play a crucial role in the future of warehousing. However, unlike robots, human operators have specific skills, conduct, and perceptions, which are only partly accounted for in current planning models. This review adopts a multimethod approach to identify and analyse how these phenomena are currently integrated into OP planning problems. In addition, we assess the relevance and adequacy of human factors modelling in academic literature with practice-based insights gathered via semi-structured interviews. This leads to five major human factors integration constructs and dedicated recommendations on how to refine them. We then take the analysis one step further and make suggestions on how to integrate these constructs with leading research methodologies in the context of Industry 5.0. The results highlight the prevalent need to increasingly account for psychosocial phenomena and their impact on operational performance. Future research opportunities provide a substantiated foundation to assist in human-centric work design.

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Section: Dynamic Varying Work Rates In Op Literaturementioning

confidence: 99%

Section: Stochastic Worker Behaviour and Work Execution In Op Literaturementioning

confidence: 99%