Simulation of the effects of different skill learning pathways in heterogeneous construction crews

Sheng Xu; Lieyun Ding

doi:10.3934/jimo.2015.11.381

Article Contents

2015, Volume 11, Issue 2: 381-397. Doi: 10.3934/jimo.2015.11.381

This issue Previous Article Credibility models with dependence structure over risks and time horizon Next Article Coordination of production and transportation in supply chain scheduling

Simulation of the effects of different skill learning pathways in heterogeneous construction crews

Sheng Xu^1, and
Lieyun Ding^1,

1.
School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074

Received: March 2013

Revised: August 2014

Published: April 2015

Abstract / Introduction Related Papers Cited by

Abstract

The problem of low-skilled workers is an obstacle in the development of the Chinese construction industry. Although many efforts have been focused on occupational training, there were several obstacles in implementing training sessions to labours. Other learning pathways were identified and introduced, but not many efforts could be found in the comparison of different learning pathways. This research simulated different skill learning methods in heterogeneous construction crews. With quantitative simulation, it explored general rules of skill learning and learning limits; discovered different learning patterns in heterogeneous construction workers; compared the effects of different learning methods; and identified the most beneficial learning pathways for workers with different learning patterns. A pre-modelling survey was conducted to determine the distributions of parameters. A network model was built to describe group interaction. Nodes in the network represented individual workers learning from repetitive work and influenced by training sessions and interpersonal communication. Results show that (a) besides training sessions, automatic on-the-job training and interactive learning from co-workers are also sources for working knowledge; (b) workers can be categorized into 5 groups according to their knowledge accumulation patterns; (c) formal training sessions and informal interactive social learning have different impact on workers with different accumulation patterns. The main contribution of this research is that it is among the firsts to discuss and simulate the multi-sourced learning process as supplements to skill trainings, identify different learning patterns corresponding to different learning groups, and make comparisons to give guidance on targeted learning strategies.

Keywords:

Mathematics Subject Classification: Primary: 90B15; Secondary: 90C39.

Citation:

Related Papers

Cited by

References

[1]	Research report on China's migrant workers, 2006, Beijing: China Yanshi Press.
[2]	A. Alvanchi, S. Lee and S. M. AbouRizk, Dynamics of workforce skill evolution in construction projects, Canadian Journal of Civil Engineering, 39 (2012), 1005-1017.
[3]	Catherine Delgoulet, Corinne Gaudart and Karine Chassaing, Entering the workforce and on-the-job skills acquisition in the construction sector, Work, 41 (2012), 155-164.
[4]	J. Chen, J. E. Taylor and H. I. Unsal, Simulating the effect of learning decay on adaptation performance in project networks, Simulation Conference (WSC), Proceedings of the 2009 Winter, (2009), 2712-2722.doi: 10.1109/WSC.2009.5429257.
[5]	P. Chinowsky, J. Diekmann and V. Galotti, Social network model of construction, Journal of Construction Engineering and Management, 134 (2008), 804-812.doi: 10.1061/(ASCE)0733-9364(2008)134:10(804).
[6]	P. S. Chinowsky, J. Diekmann and J. O'Brien, Project organizations as social networks, Journal of Construction Engineering and Management, 136 (2010), 452-458.doi: 10.1061/(ASCE)CO.1943-7862.0000161.
[7]	R. Cowana, N. Jonardb and M. Ozman, Knowledge dynamics in a network industry, Technological Forecasting and Social Change, 71 (2004), 469-484.doi: 10.1016/S0040-1625(03)00045-3.
[8]	J. M. Dutton and A. Thomas, Treating progress functions as a managerial opportunity, Academy of Management Review, 9 (1984), 235-247.doi: 10.2307/258437.
[9]	A. Edmondson, R. Bohmer and G. Pisano, Disrupted routines: Team learning and new technology implementation in hospitals, Administrative Science Quarterly, 46 (2001), 685-716.doi: 10.2307/3094828.
[10]	G. Fioretti, The organizational learning curve, European Journal of Operational Research, 177 (2007), 1375-1384.doi: 10.1016/j.ejor.2005.04.009.
[11]	M. Fisher and P. Roben, Organizational learning and vocational education and training, 2004, University of Bremen.
[12]	I. Grugulis and D. Stoyanova, Skill and performance, British Journal of Industrial Relations, 49 (2011), 515-536.doi: 10.1111/j.1467-8543.2010.00779.x.
[13]	N. W. Hatch and D. C. Mowery, Process innovation and learning by doing in semiconductor manufacturing, Management Science, 44 (1998), 1461-1477.doi: 10.1287/mnsc.44.11.1461.
[14]	K. N. Hewage, A. Gannoruwa and J. Y. Ruwanpura, Current status of factors leading to team performance of on-site construction professionals in Alberta building construction projects, Canadian Journal of Civil Engineering, 38 (2011), 679-689.doi: 10.1139/l11-038.
[15]	J. Hinze and S. Olbina, Empirical analysis of the learning curve principle in prestressed concrete piles, Journal of Construction Engineering and Management, 135 (2009), 425-431.doi: 10.1061/(ASCE)CO.1943-7862.0000004.
[16]	R. S. Huckman, B. R. Staats and D. M. Upton, Team familiarity, role experience, and performance: Evidence from indian software services, Management Science, 55 (2009), 85-100.doi: 10.1287/mnsc.1080.0921.
[17]	N. Iskander and N. Lowe, Hidden talent: Tacit skill formation and labor market incorporation of latino immigrants in the United States, Journal of Planning Education and Research, 30 (2010), 132-146.
[18]	A. M. Jarkas, Critical investigation into the applicability of the learning curve theory to rebar fixing labor productivity, Journal of Construction Engineering and Management 136 (2010), 1279-1288.doi: 10.1061/(ASCE)CO.1943-7862.0000236.
[19]	H. Ju, Thinking on China's construction industry vocational training of migrant workers (In Chinese), China Construction News, (Oct. 16,2007).
[20]	S. F. Kelsey, et al., Effect of investigator experience on percutaneous transluminal coronary angioplasty, American Journal of Cardiology, 53 (1984), C56-C64.doi: 10.1016/0002-9149(84)90747-1.
[21]	A. Kerckhoff, S. Raudenbush and E. Glennie, Education, cognitive skill, and labor force outcomes, Sociology and Education, 74 (2001), 1-24.doi: 10.2307/2673142.
[22]	D. Z. Levin, Organizational learning and the transfer of knowledge: An investigation of quality improvement, Organization Science, 11 (2000), 630-647.doi: 10.1287/orsc.11.6.630.12535.
[23]	H. Li, P. E. D. Love and D. S. Drew, Effects of overtime work and additional resources on project cost and quality. Engineering, Construction and Architectural Management, 7 (2000), 211-220.
[24]	F. Luo, Training issue of Chinese migrant workers in construction, Shanxi Construction, 34 (2008), 175-177.
[25]	J. Ma, et al, China statistical yearbook. 2010 [cited 2011 January 15], Available from: http://www.stats.gov.cn/tjsj/ndsj/2010/indexch.htm.
[26]	T. Maurer, Career-relevant learning and development, worker age, and beliefs about self-efficacy for development, Journal of Management, 27 (2001), 123-140.doi: 10.1016/S0149-2063(00)00092-1.
[27]	Z. Mi, A study on the quality of vocational training of peasant workers (in Chinese), 2008, Capital University of Economics and Business: Beijing.
[28]	L. Muchnik, et al., Self-emergence of knowledge trees: Extraction of the Wikipedia hierarchies, Physics Review E, 76 (2007), 016106.doi: 10.1103/PhysRevE.76.016106.
[29]	D. A. Nembhard and S. M. Shafer, The effects of workforce heterogeneity on productivity in an experiential learning environment, International Journal of Production Research, 46 (2008), 3909-3929.doi: 10.1080/00207540600596981.
[30]	D. A. Nembhard and M. V. Uzumeri, Experiential learning and forgetting for manual and cognitive tasks, International Journal of Industrial Ergonomics, 25 (2000), 315-326.doi: 10.1016/S0169-8141(99)00021-9.
[31]	D. A. Nembhard and M. V. Uzumeri, An individual-based description of learning within an organization, Transactions on Engineering Management, 47 (2000), 370-378.doi: 10.1109/17.865905.
[32]	S. Ogunlana, H. Li and F. Sukhera, System dynamics approach to exploring performance enhancement in a construction organization, Journal of Construction Engineering and Management, 129 (2003), 528-536.doi: 10.1061/(ASCE)0733-9364(2003)129:5(528).
[33]	F. Peña-Mora and M. Li, Dynamic planning and control methodology for design/build fast-track construction projects, Journal of Construction Engineering and Management, 127 (2001), 1-17.
[34]	L. Rapping, Learning and World War II production functions, Review of Economics and Statistics, 47 (1965), 81-86.doi: 10.2307/1924126.
[35]	R. Reagans, L. Argote and D. Brooks, Individual experience and experience working together: Predicting learning rates from knowing who knows what and knowing how to work together, Management Science, 51 (2005), 869-881.doi: 10.1287/mnsc.1050.0366.
[36]	Á. Rebuge and D. R. Ferreira, Business process analysis in healthcare environments: A methodology based on process mining, Information Systems, 37 (2012), 99-116.doi: 10.1016/j.is.2011.01.003.
[37]	S. M. Shafer, D. A. Nembhard and M. V. Uzumeri, The effects of worker learning, forgetting, and heterogeneity on assembly line productivity, Management Science, 47 (2001), 1639-1653.doi: 10.1287/mnsc.47.12.1639.10236.
[38]	J. D. Sterman, System dynamics modeling for project management, 1992, MIT System Dynamics Group: Cambridge, Mass., USA.
[39]	R. Sun, E. Merrill and T. Peterson, From implicit skills to explicit knowledge: A bottom-up model of skill learning, Cognitive Science, 25 (2001), 203-244.doi: 10.1016/S0364-0213(01)00035-0.
[40]	J. E. Taylor and R. Levitt, Simulating learning dynamics in project networks, Journal of Construction Engineering and Management, 135 (2009), 1009-1015.doi: 10.1061/(ASCE)CO.1943-7862.0000065.
[41]	H. R. Thomas, Construction learning curves, Practice Periodical on Structural Design and Construction, 14 (2009), 14-20.doi: 10.1061/(ASCE)1084-0680(2009)14:1(14).
[42]	M. Uzumeri and D. Nembhard, A population of learners: A new way to measure organizational learning, Journal of Operations Management, 16 (1998), 515-528.doi: 10.1016/S0272-6963(97)00017-X.
[43]	C. Winch and L. Clarke, 'Front-Loaded' vocational education versus lifelong learning: A critique of current UK government policy, Oxford Review of Education, 29 (2003), 239-252.doi: 10.1080/0305498032000080701.
[44]	P. S. P. Wong, S. O. Cheung and C. Hardcastle, Embodying learning effect in performance prediction, Journal of Construction Engineering and Management, 133 (2007), 474-482.doi: 10.1061/(ASCE)0733-9364(2007)133:6(474).
[45]	T. P. Wright, Factors affecting the cost of airplanes, Journal of Aeronautic Science, 3 (1936), 122-128.
[46]	Y. Xu, A case study on the improvement of vocational training system following the new trend of public management-taking Shanghai Xuhui District as an example (in Chinese), 2007, Shanghai Jiaotong University: Shanghai.
[47]	Y. Zhang, A research on migrant workers's training input decision-making, in Agricultural Economics and Management2008, Nanjing Agrieultural University: Nanjing.