TY - JOUR
T1 - Strategic Workforce Planning for Production of Prefabricated Bathroom Units
T2 - An Advanced Markovian Approach
AU - Han, Jinchi
AU - Chen, Chen
AU - Tiong, Robert Lee Kong
AU - Wu, Kan
AU - Chew, Daryl Kok Hoong
N1 - Publisher Copyright:
© 2024 American Society of Civil Engineers.
PY - 2024/8/1
Y1 - 2024/8/1
N2 - Producing prefabricated bathroom units (PBUs) involves a dry installation method that heavily relies on human labor. Therefore, strategically accomplishing efficient and agile workforce planning emphasizes the critical significance. However, previous studies that address workforce planning often overlook the long-term stochastic effects and assume a homogeneous workforce for the sake of computational simplicity. To overcome these limitations, this study adopts the Markovian approach to establish an explicit relationship between workforce cost and cycle time, considering a heterogeneous workforce and uncertain human-caused events. The proposed model has a hierarchical structure that addresses the behavioral tendencies that drive task allocation at the individual level and the aggregate effect of manpower allocation at the operation level. By integrating Little's law in queuing theory and metaheuristics optimization, the cycle time can be calculated while searching for the optimal workforce arrangement configuration. The study cross-validated the computational results with empirical data from a precast factory in Singapore and conducted a sensitivity analysis to verify the reliability. The results show that cross-training workers to multiple skills can lead to significant time savings, with a maximum of roughly 23 h saved in PBU cycle time. Ultimately, this research contributes to the body of knowledge by proposing a strategic workforce planning model that accounts for a heterogeneous workforce and uncertain human-caused events. It utilizes a partial cross-training configuration strategy to maximize productivity and flexibility for PBU production.
AB - Producing prefabricated bathroom units (PBUs) involves a dry installation method that heavily relies on human labor. Therefore, strategically accomplishing efficient and agile workforce planning emphasizes the critical significance. However, previous studies that address workforce planning often overlook the long-term stochastic effects and assume a homogeneous workforce for the sake of computational simplicity. To overcome these limitations, this study adopts the Markovian approach to establish an explicit relationship between workforce cost and cycle time, considering a heterogeneous workforce and uncertain human-caused events. The proposed model has a hierarchical structure that addresses the behavioral tendencies that drive task allocation at the individual level and the aggregate effect of manpower allocation at the operation level. By integrating Little's law in queuing theory and metaheuristics optimization, the cycle time can be calculated while searching for the optimal workforce arrangement configuration. The study cross-validated the computational results with empirical data from a precast factory in Singapore and conducted a sensitivity analysis to verify the reliability. The results show that cross-training workers to multiple skills can lead to significant time savings, with a maximum of roughly 23 h saved in PBU cycle time. Ultimately, this research contributes to the body of knowledge by proposing a strategic workforce planning model that accounts for a heterogeneous workforce and uncertain human-caused events. It utilizes a partial cross-training configuration strategy to maximize productivity and flexibility for PBU production.
KW - Markov Chains
KW - Offsite construction
KW - Prefabricated bathroom unit (PBU)
KW - Strategic planning
KW - Workforce planning
UR - http://www.scopus.com/inward/record.url?scp=85196370221&partnerID=8YFLogxK
U2 - 10.1061/JCEMD4.COENG-14514
DO - 10.1061/JCEMD4.COENG-14514
M3 - 文章
AN - SCOPUS:85196370221
SN - 0733-9364
VL - 150
JO - Journal of Construction Engineering and Management
JF - Journal of Construction Engineering and Management
IS - 8
M1 - 04024097
ER -