Effect of learning on line-of-balance scheduling
By: ARDITI, David
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Contributor(s): TOKDEMIR, Onur Behzat | SUH, Kangsuk.
Material type: 
ArticlePublisher: 2001International Journal of Project Management 19, 5, p. 65-277Abstract: An approach to formulate learning rates and include them in line-of-balance (LOB) calculations is proposed in this paper. Learning rates are geneated by modifying historical learning rates of typical construction activities and by incorporating the impact of relevant factors such as, number of operations in one unit, activity complexity, and job and managment conditions. Fuzzy set theory is used to develop production rules to treat both factual and uncertain information. An S-type membership function is used to interpret to fuzzy data produce adjustment factors that are in turn used to modify consecutive learning rtes, until an adjusted learning rate is obtained. The adjusted learning rate is then used to calculate expected worker-hours and activity durations at each unitof production (e.g., a floor in a high-rise building, a mile of pavement work, etc.) A final LOB diagram is generated using this information. Different paris of curves represent the start and the finish times of each activity in sets of units that make use of different numbers of crews. Learning reduces project duration and resource requirements. The proposed approach demonstrates the potential ofor formalizing the inclusion of learning effects into the LOB scheduling of repetitive-unit construction
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An approach to formulate learning rates and include them in line-of-balance (LOB) calculations is proposed in this paper. Learning rates are geneated by modifying historical learning rates of typical construction activities and by incorporating the impact of relevant factors such as, number of operations in one unit, activity complexity, and job and managment conditions. Fuzzy set theory is used to develop production rules to treat both factual and uncertain information. An S-type membership function is used to interpret to fuzzy data produce adjustment factors that are in turn used to modify consecutive learning rtes, until an adjusted learning rate is obtained. The adjusted learning rate is then used to calculate expected worker-hours and activity durations at each unitof production (e.g., a floor in a high-rise building, a mile of pavement work, etc.) A final LOB diagram is generated using this information. Different paris of curves represent the start and the finish times of each activity in sets of units that make use of different numbers of crews. Learning reduces project duration and resource requirements. The proposed approach demonstrates the potential ofor formalizing the inclusion of learning effects into the LOB scheduling of repetitive-unit construction
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