A linear programming approach to optimize strategic investment in the construction workforce
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Construction workforce management practice needs optimization techniques to better address the issue of investment in the workforce. This dissertation presents a linear programming model developed to provide an optimization-based framework for matching supply and demand of construction labor most efficiently through training, recruitment, and allocation. The developed model, entitled Optimal Workforce Investment Model (OWIM), was applied on data obtained from the CII Model Plant, a hypothetical $140-million petro-chemical project to be built in the Gulf Coast region, and a recent survey conducted by the Center for Construction Industry Studies (CCIS) and the Construction Industry Institute (CII). The input data to the model consist of a certain available labor pool, cost figures for training workers in different skills, the cost of hiring workers, hourly labor wages, and estimates of affinities between the different considered skills. The output of the OWIM includes the number of construction workers to be hired and the number of construction workers to be trained in order to meet a job-site demand pattern over a certain period of time in the most cost-effective fashion. Use of the model not only helps to alleviate problems of skilled labor shortages in the US construction industry but also provides firms costs savings by reducing hiring, training, and retainage costs.