The Integrated Problem of Construction Project Scheduling and Multiskilled Staff Assignment with Learning Effect

Abstract

Effective multiskill resource scheduling requires comprehensive workforce assignments. Drawing on the multiskill project scheduling problem perspective, this study investigates the integrated problem of project scheduling and multiskilled staffing based on a more realistic perspective that human skill level is dynamic and has a variable activity duration caused by the learning effect. An integer linear programming model is innovatively constructed to solve the problem directly. Moreover, an adapted tabu search algorithm with six resource-oriented priority strategies is designed to solve large-scale problems efficiently. Based on a realistic construction project case and numerical instances, comprehensive computational experiments are conducted to validate the proposed algorithm. The findings reveal the impact of different resource strategies on project duration and personal skill growth, particularly through the optimization of project scheduling according to the dynamic scenarios. The research extends conventional project scheduling knowledge and its methods by solving complex scheduling problems for improved project objectives and employee value. In practice, the model and the algorithm can be integrated into the project management information system for strategizing a plan of project scheduling and staff assignment proactively. This study develops a linear optimizing model and an efficient tabu search algorithm for the integrated problem of project scheduling and staffing with multiskilled personnel in construction project management. The model and the proposed algorithm can be applied to advance any digitalized scheduling platforms; further, project managers can be supported to flexibly select appropriate methods to deal with resource problems under different situations, workforce types, and time–skill development trade-off. For small-scale problems, users can simply input the model parameters, including information of project, activity, and employees; then, the optimal integrated scheme of activity executing time, staffing, and applied skill type can be obtained automatically through IBM ILOG CPLEX Optimizer software methods. For large-scale problems, after imputing problem parameters and setting the algorithm parameters, such as the resource allocation strategy and iteration number, the tabu search algorithm can be implemented to yield a satisfactory and efficient integrated scheme. In summary, this work provides project construction planners or managers with an innovative decision-support tool for solving complex but practical scheduling problems.