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Al-Bayati, A J, Abudayyeh, O, Fredericks, T and Butt, S E (2017) Reducing Fatality Rates of the Hispanic Workforce in the U.S. Construction Industry: Challenges and Strategies. Journal of Construction Engineering and Management, 143(03).
Awolusi, I G and Marks, E D (2017) Safety Activity Analysis Framework to Evaluate Safety Performance in Construction. Journal of Construction Engineering and Management, 143(03).
Babar, S, Thaheem, M J and Ayub, B (2017) Estimated Cost at Completion: Integrating Risk into Earned Value Management. Journal of Construction Engineering and Management, 143(03).
Kam, C, Song, M H and Senaratna, D (2017) {[}VDC Scorecard{]}: Formulation, Application, and Validation. Journal of Construction Engineering and Management, 143(03).
Kisi, K P, Mani, N, Rojas, E M and Foster, E T (2017) Optimal Productivity in Labor-Intensive Construction Operations: Pilot Study. Journal of Construction Engineering and Management, 143(03).
- Type: Journal Article
- Keywords: Productivity; Construction management; Simulation; Methodology; Labor and personnel issues;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001257
- Abstract:
Optimal productivity is the highest sustainable productivity level achievable under good management and typical working conditions. Accordingly, optimal productivity provides the foundation for determining the absolute efficiency of construction operations because an accurate estimate of optimal productivity enables the comparison between actual versus optimal rather than actual versus historical productivity. This research contributes to the current body of knowledge by introducing a two-prong strategy for estimating optimal productivity in labor-intensive construction operations and by applying this strategy to a pilot study on the replacement of electrical lighting fixtures. The first prong, or top-down approach, estimates the upper limit of optimal productivity by introducing system inefficiencies into the productivity frontier—the productivity achieved under perfect conditions. This study uses a qualitative factor model to identify this upper limit. The second prong, or bottom-up approach, estimates the lower limit of optimal productivity by taking away operational inefficiencies from actual productivity—productivity recorded in the field. A discrete-event simulation model provides this lower-limit value. An average of the upper and lower limits yields the best estimate of optimal productivity. This paper reviews relevant literature, presents the details of both the top-down and bottom-up approaches, analyzes the data from a pilot project, evaluates the feasibility of this two-prong strategy, and finally provides a novel framework for project managers who want to accurately estimate the optimal productivity of their labor-intensive construction operations.
Li, Y, Wang, X and Wang, Y (2017) Using Bargaining Game Theory for Risk Allocation of Public-Private Partnership Projects: Insights from Different Alternating Offer Sequences of Participants. Journal of Construction Engineering and Management, 143(03).
Louis, J and Dunston, P S (2017) Methodology for Real-Time Monitoring of Construction Operations Using Finite State Machines and Discrete-Event Operation Models. Journal of Construction Engineering and Management, 143(03).
Niu, Y, Lu, W, Liu, D, Chen, K, Anumba, C and Huang, G G (2017) An SCO-Enabled Logistics and Supply Chain–Management System in Construction. Journal of Construction Engineering and Management, 143(03).
Shrestha, P P and Fernane, J D (2017) Performance of Design-Build and Design-Bid-Build Projects for Public Universities. Journal of Construction Engineering and Management, 143(03).
Yoon, Y, Patel, S, Ji, R and Hastak, M (2017) Current State of Reflective Cracking in the United States. Journal of Construction Engineering and Management, 143(03).