American Institute of Mathematical Sciences

Advanced
April  2015, 11(2): 381-397. doi: 10.3934/jimo.2015.11.381

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, China, China

Received  March 2013 Revised  August 2014 Published  September 2014

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: network, skill learning, Construction workers, trianing., simulation.
Mathematics Subject Classification: Primary: 90B15; Secondary: 90C3.
Citation: Sheng Xu, Lieyun Ding. Simulation of the effects of different skill learning pathways in heterogeneous construction crews. Journal of Industrial & Management Optimization, 2015, 11 (2) : 381-397. doi: 10.3934/jimo.2015.11.381
References:
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[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.   Google Scholar

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Catherine Delgoulet, Corinne Gaudart and Karine Chassaing, Entering the workforce and on-the-job skills acquisition in the construction sector,, Work, 41 (2012), 155.   Google Scholar

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P. S. Chinowsky, J. Diekmann and J. O'Brien, Project organizations as social networks,, Journal of Construction Engineering and Management, 136 (2010), 452.  doi: 10.1061/(ASCE)CO.1943-7862.0000161.  Google Scholar

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R. Cowana, N. Jonardb and M. Ozman, Knowledge dynamics in a network industry,, Technological Forecasting and Social Change, 71 (2004), 469.  doi: 10.1016/S0040-1625(03)00045-3.  Google Scholar

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J. M. Dutton and A. Thomas, Treating progress functions as a managerial opportunity,, Academy of Management Review, 9 (1984), 235.  doi: 10.2307/258437.  Google Scholar

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A. Edmondson, R. Bohmer and G. Pisano, Disrupted routines: Team learning and new technology implementation in hospitals,, Administrative Science Quarterly, 46 (2001), 685.  doi: 10.2307/3094828.  Google Scholar

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G. Fioretti, The organizational learning curve,, European Journal of Operational Research, 177 (2007), 1375.  doi: 10.1016/j.ejor.2005.04.009.  Google Scholar

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M. Fisher and P. Roben, Organizational learning and vocational education and training,, 2004, ().   Google Scholar

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I. Grugulis and D. Stoyanova, Skill and performance,, British Journal of Industrial Relations, 49 (2011), 515.  doi: 10.1111/j.1467-8543.2010.00779.x.  Google Scholar

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N. W. Hatch and D. C. Mowery, Process innovation and learning by doing in semiconductor manufacturing,, Management Science, 44 (1998), 1461.  doi: 10.1287/mnsc.44.11.1461.  Google Scholar

[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.  doi: 10.1139/l11-038.  Google Scholar

[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.  doi: 10.1061/(ASCE)CO.1943-7862.0000004.  Google Scholar

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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), 136 (2010), 1279.  doi: 10.1061/(ASCE)CO.1943-7862.0000236.  Google Scholar

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H. Ju, Thinking on China's construction industry vocational training of migrant workers (In Chinese),, China Construction News, ().   Google Scholar

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S. F. Kelsey, et al., Effect of investigator experience on percutaneous transluminal coronary angioplasty,, American Journal of Cardiology, 53 (1984).  doi: 10.1016/0002-9149(84)90747-1.  Google Scholar

[21]

A. Kerckhoff, S. Raudenbush and E. Glennie, Education, cognitive skill, and labor force outcomes,, Sociology and Education, 74 (2001), 1.  doi: 10.2307/2673142.  Google Scholar

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D. Z. Levin, Organizational learning and the transfer of knowledge: An investigation of quality improvement,, Organization Science, 11 (2000), 630.  doi: 10.1287/orsc.11.6.630.12535.  Google Scholar

[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.   Google Scholar

[24]

F. Luo, Training issue of Chinese migrant workers in construction,, Shanxi Construction, 34 (2008), 175.   Google Scholar

[25]

J. Ma, et al, China statistical yearbook. 2010 [cited 2011 January 15],, Available from: , ().   Google Scholar

[26]

T. Maurer, Career-relevant learning and development, worker age, and beliefs about self-efficacy for development,, Journal of Management, 27 (2001), 123.  doi: 10.1016/S0149-2063(00)00092-1.  Google Scholar

[27]

Z. Mi, A study on the quality of vocational training of peasant workers (in Chinese),, 2008, ().   Google Scholar

[28]

L. Muchnik, et al., Self-emergence of knowledge trees: Extraction of the Wikipedia hierarchies,, Physics Review E, 76 (2007).  doi: 10.1103/PhysRevE.76.016106.  Google Scholar

[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.  doi: 10.1080/00207540600596981.  Google Scholar

[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.  doi: 10.1016/S0169-8141(99)00021-9.  Google Scholar

[31]

D. A. Nembhard and M. V. Uzumeri, An individual-based description of learning within an organization,, Transactions on Engineering Management, 47 (2000), 370.  doi: 10.1109/17.865905.  Google Scholar

[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.  doi: 10.1061/(ASCE)0733-9364(2003)129:5(528).  Google Scholar

[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.   Google Scholar

[34]

L. Rapping, Learning and World War II production functions,, Review of Economics and Statistics, 47 (1965), 81.  doi: 10.2307/1924126.  Google Scholar

[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.  doi: 10.1287/mnsc.1050.0366.  Google Scholar

[36]

Á. Rebuge and D. R. Ferreira, Business process analysis in healthcare environments: A methodology based on process mining,, Information Systems, 37 (2012), 99.  doi: 10.1016/j.is.2011.01.003.  Google Scholar

[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.  doi: 10.1287/mnsc.47.12.1639.10236.  Google Scholar

[38]

J. D. Sterman, System dynamics modeling for project management,, 1992, ().   Google Scholar

[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.  doi: 10.1016/S0364-0213(01)00035-0.  Google Scholar

[40]

J. E. Taylor and R. Levitt, Simulating learning dynamics in project networks,, Journal of Construction Engineering and Management, 135 (2009), 1009.  doi: 10.1061/(ASCE)CO.1943-7862.0000065.  Google Scholar

[41]

H. R. Thomas, Construction learning curves,, Practice Periodical on Structural Design and Construction, 14 (2009), 14.  doi: 10.1061/(ASCE)1084-0680(2009)14:1(14).  Google Scholar

[42]

M. Uzumeri and D. Nembhard, A population of learners: A new way to measure organizational learning,, Journal of Operations Management, 16 (1998), 515.  doi: 10.1016/S0272-6963(97)00017-X.  Google Scholar

[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.  doi: 10.1080/0305498032000080701.  Google Scholar

[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.  doi: 10.1061/(ASCE)0733-9364(2007)133:6(474).  Google Scholar

[45]

T. P. Wright, Factors affecting the cost of airplanes,, Journal of Aeronautic Science, 3 (1936), 122.   Google Scholar

[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, ().   Google Scholar

[47]

Y. Zhang, A research on migrant workers's training input decision-making,, in Agricultural Economics and Management2008, ().   Google Scholar

show all references

References:
[1]

, Research report on China's migrant workers,, 2006, ().   Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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), (2009), 2712.  doi: 10.1109/WSC.2009.5429257.  Google Scholar

[5]

P. Chinowsky, J. Diekmann and V. Galotti, Social network model of construction,, Journal of Construction Engineering and Management, 134 (2008), 804.  doi: 10.1061/(ASCE)0733-9364(2008)134:10(804).  Google Scholar

[6]

P. S. Chinowsky, J. Diekmann and J. O'Brien, Project organizations as social networks,, Journal of Construction Engineering and Management, 136 (2010), 452.  doi: 10.1061/(ASCE)CO.1943-7862.0000161.  Google Scholar

[7]

R. Cowana, N. Jonardb and M. Ozman, Knowledge dynamics in a network industry,, Technological Forecasting and Social Change, 71 (2004), 469.  doi: 10.1016/S0040-1625(03)00045-3.  Google Scholar

[8]

J. M. Dutton and A. Thomas, Treating progress functions as a managerial opportunity,, Academy of Management Review, 9 (1984), 235.  doi: 10.2307/258437.  Google Scholar

[9]

A. Edmondson, R. Bohmer and G. Pisano, Disrupted routines: Team learning and new technology implementation in hospitals,, Administrative Science Quarterly, 46 (2001), 685.  doi: 10.2307/3094828.  Google Scholar

[10]

G. Fioretti, The organizational learning curve,, European Journal of Operational Research, 177 (2007), 1375.  doi: 10.1016/j.ejor.2005.04.009.  Google Scholar

[11]

M. Fisher and P. Roben, Organizational learning and vocational education and training,, 2004, ().   Google Scholar

[12]

I. Grugulis and D. Stoyanova, Skill and performance,, British Journal of Industrial Relations, 49 (2011), 515.  doi: 10.1111/j.1467-8543.2010.00779.x.  Google Scholar

[13]

N. W. Hatch and D. C. Mowery, Process innovation and learning by doing in semiconductor manufacturing,, Management Science, 44 (1998), 1461.  doi: 10.1287/mnsc.44.11.1461.  Google Scholar

[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.  doi: 10.1139/l11-038.  Google Scholar

[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.  doi: 10.1061/(ASCE)CO.1943-7862.0000004.  Google Scholar

[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.  doi: 10.1287/mnsc.1080.0921.  Google Scholar

[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.   Google Scholar

[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), 136 (2010), 1279.  doi: 10.1061/(ASCE)CO.1943-7862.0000236.  Google Scholar

[19]

H. Ju, Thinking on China's construction industry vocational training of migrant workers (In Chinese),, China Construction News, ().   Google Scholar

[20]

S. F. Kelsey, et al., Effect of investigator experience on percutaneous transluminal coronary angioplasty,, American Journal of Cardiology, 53 (1984).  doi: 10.1016/0002-9149(84)90747-1.  Google Scholar

[21]

A. Kerckhoff, S. Raudenbush and E. Glennie, Education, cognitive skill, and labor force outcomes,, Sociology and Education, 74 (2001), 1.  doi: 10.2307/2673142.  Google Scholar

[22]

D. Z. Levin, Organizational learning and the transfer of knowledge: An investigation of quality improvement,, Organization Science, 11 (2000), 630.  doi: 10.1287/orsc.11.6.630.12535.  Google Scholar

[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.   Google Scholar

[24]

F. Luo, Training issue of Chinese migrant workers in construction,, Shanxi Construction, 34 (2008), 175.   Google Scholar

[25]

J. Ma, et al, China statistical yearbook. 2010 [cited 2011 January 15],, Available from: , ().   Google Scholar

[26]

T. Maurer, Career-relevant learning and development, worker age, and beliefs about self-efficacy for development,, Journal of Management, 27 (2001), 123.  doi: 10.1016/S0149-2063(00)00092-1.  Google Scholar

[27]

Z. Mi, A study on the quality of vocational training of peasant workers (in Chinese),, 2008, ().   Google Scholar

[28]

L. Muchnik, et al., Self-emergence of knowledge trees: Extraction of the Wikipedia hierarchies,, Physics Review E, 76 (2007).  doi: 10.1103/PhysRevE.76.016106.  Google Scholar

[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.  doi: 10.1080/00207540600596981.  Google Scholar

[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.  doi: 10.1016/S0169-8141(99)00021-9.  Google Scholar

[31]

D. A. Nembhard and M. V. Uzumeri, An individual-based description of learning within an organization,, Transactions on Engineering Management, 47 (2000), 370.  doi: 10.1109/17.865905.  Google Scholar

[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.  doi: 10.1061/(ASCE)0733-9364(2003)129:5(528).  Google Scholar

[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.   Google Scholar

[34]

L. Rapping, Learning and World War II production functions,, Review of Economics and Statistics, 47 (1965), 81.  doi: 10.2307/1924126.  Google Scholar

[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.  doi: 10.1287/mnsc.1050.0366.  Google Scholar

[36]

Á. Rebuge and D. R. Ferreira, Business process analysis in healthcare environments: A methodology based on process mining,, Information Systems, 37 (2012), 99.  doi: 10.1016/j.is.2011.01.003.  Google Scholar

[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.  doi: 10.1287/mnsc.47.12.1639.10236.  Google Scholar

[38]

J. D. Sterman, System dynamics modeling for project management,, 1992, ().   Google Scholar

[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.  doi: 10.1016/S0364-0213(01)00035-0.  Google Scholar

[40]

J. E. Taylor and R. Levitt, Simulating learning dynamics in project networks,, Journal of Construction Engineering and Management, 135 (2009), 1009.  doi: 10.1061/(ASCE)CO.1943-7862.0000065.  Google Scholar

[41]

H. R. Thomas, Construction learning curves,, Practice Periodical on Structural Design and Construction, 14 (2009), 14.  doi: 10.1061/(ASCE)1084-0680(2009)14:1(14).  Google Scholar

[42]

M. Uzumeri and D. Nembhard, A population of learners: A new way to measure organizational learning,, Journal of Operations Management, 16 (1998), 515.  doi: 10.1016/S0272-6963(97)00017-X.  Google Scholar

[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.  doi: 10.1080/0305498032000080701.  Google Scholar

[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.  doi: 10.1061/(ASCE)0733-9364(2007)133:6(474).  Google Scholar

[45]

T. P. Wright, Factors affecting the cost of airplanes,, Journal of Aeronautic Science, 3 (1936), 122.   Google Scholar

[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, ().   Google Scholar

[47]

Y. Zhang, A research on migrant workers's training input decision-making,, in Agricultural Economics and Management2008, ().   Google Scholar

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