Optimization of Job Allocation in Construction Organizations to Maximize Workers’ Career Development Opportunities
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 6
Abstract
Workforce planning in the construction industry too often ignores the symbiotic relationship between employee and employer objectives by overly concentrating on corporate objectives such as maximizing productivity at the expense of construction workers’ career development needs. Overall, the consequence of this approach is suboptimal performance. To address this problem, this paper presents an innovative multiobjective model that enables managers to optimize the relationship between these interdependent corporate priorities. The proposed model was implemented and solved using mixed-integer nonlinear programming on a case study involving the allocation of tasks to employees with different skill levels in a multidisciplinary engineering consulting company. While leading to a small loss of productivity, the results show a significant improvement in the career development of workers compared to conventional productivity-oriented workforce planning models, with on average 8.6% improvement in employees’ closeness to their ideal skill set. Furthermore, the model produced Pareto-optimal points and a Pareto curve that enabled client-model users to select optimum job allocation based on their preferences. This research represents a paradigm shift toward a new class of socially responsible workforce planning models in which the objectives of both employees and employers are optimized.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
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Journal of Construction Engineering and Management
Volume 145 • Issue 6 • June 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jun 27, 2018
Accepted: Nov 2, 2018
Published online: Apr 15, 2019
Published in print: Jun 1, 2019
Discussion open until: Sep 15, 2019
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