Details
| Original language | English |
|---|---|
| Pages (from-to) | 335-342 |
| Number of pages | 8 |
| Journal | Production Engineering |
| Volume | 15 |
| Issue number | 3-4 |
| Early online date | 28 Jan 2021 |
| Publication status | Published - Jun 2021 |
Abstract
Keywords
- Employee deployment, Operational planning, Optimization algorithm, Production management, Task assignment
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Production Engineering, Vol. 15, No. 3-4, 06.2021, p. 335-342.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A comparison of methods for determining performance based employee deployment in production systems
AU - Ast, Jonas
AU - Wasseghi, Raed
AU - Nyhuis, Peter
N1 - Funding Information: This publication is written as part of the project “teamIn”. The research and development project is funded by the German Federal Ministry of Education and Research (BMBF) and the European Social Fund (ESF) within the program “Future of Work” (Grant no.: 02L18A140) and is managed by the Project Management Agency Karlsruhe (PTKA). The author is responsible for the content of this publication.
PY - 2021/6
Y1 - 2021/6
N2 - Employee deployment is a crucial process in production systems. Based on qualification and individual performance of employees, deployment decisions can lead to ambiguous outcomes. This paper first reviews the state of the art and further compares two methods based on combinatorial analysis for employee deployment. Therefore, this paper emphasizes the costs and benefits of a Brute Force and an alternative Greedy method. When considering the qualification and individual performance of each employee, both algorithms provide working solutions. In direct comparison, the outcome of the alternative Greedy algorithm is more efficient in terms of calculation time whereas the Brute Force method provides the combination with the global optimum. This means calculation time as well as quality of outcome differ. The exponential growth of employee allocation possibilities depends on the amount of employees and leads to high calculation times, when using a Brute Force method. The comparison of both methods reveal that the proposed alternative Greedy algorithm reaches nearly as high outcomes as the Brute Force does, with significantly less calculation time. Furthermore, this paper offers an insight into the impact of deployment decisions within production systems.
AB - Employee deployment is a crucial process in production systems. Based on qualification and individual performance of employees, deployment decisions can lead to ambiguous outcomes. This paper first reviews the state of the art and further compares two methods based on combinatorial analysis for employee deployment. Therefore, this paper emphasizes the costs and benefits of a Brute Force and an alternative Greedy method. When considering the qualification and individual performance of each employee, both algorithms provide working solutions. In direct comparison, the outcome of the alternative Greedy algorithm is more efficient in terms of calculation time whereas the Brute Force method provides the combination with the global optimum. This means calculation time as well as quality of outcome differ. The exponential growth of employee allocation possibilities depends on the amount of employees and leads to high calculation times, when using a Brute Force method. The comparison of both methods reveal that the proposed alternative Greedy algorithm reaches nearly as high outcomes as the Brute Force does, with significantly less calculation time. Furthermore, this paper offers an insight into the impact of deployment decisions within production systems.
KW - Employee deployment
KW - Operational planning
KW - Optimization algorithm
KW - Production management
KW - Task assignment
UR - http://www.scopus.com/inward/record.url?scp=85099939767&partnerID=8YFLogxK
U2 - 10.1007/s11740-021-01019-5
DO - 10.1007/s11740-021-01019-5
M3 - Article
VL - 15
SP - 335
EP - 342
JO - Production Engineering
JF - Production Engineering
SN - 0944-6524
IS - 3-4
ER -