2023

Engineering Applications of Artificial Intelligence

Rocznik: 2023 | Tom: vol. 119

artykuł naukowy

angielski

EN Companies often struggle with the problem of appropriately assigning multi-skilled employees and maintaining the required skill levels while performing a changing project’s portfolio. The paper focuses on maintaining employee skills at a constant level without degradation. Maintaining the required team efficiency and competency levels through staff rotation while avoiding additional periodic training because of competency loss has been considered. The aim is to develop an analytical tool that considers individual learning and workers who irregularly perform repetitive tasks and forget their skills. Incorporating competency’s learning and forgetting curves into modelling workers’ assignments and schedules allows for more accurate estimates of actual and future workforce performance. The paper contributes to the theory by proposing a novel declarative programming approach which allows for rotating a multi-skilled team of employees to maintain their competences at a required level. The stationary declarative model that implements this approach can easily be extended to non-stationary and fuzzy variants. The decision-making problem is looking for the rotation schedule of a multi-skilled team of employees to maintain their competences at a required level when satisfying the timely fulfilment of a given project’s portfolio. The presented case study from remanufacturing industry illustrates that the proposed approach can be used for the scale of problems which occur in real-life companies.

105725-1 - 105725-18

10.1016/j.engappai.2022.105725

https://www.sciencedirect.com/science/article/pii/S0952197622007151?dgcid=author

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