A Hybrid Decision-Making Framework to Manage Human Assets in Project Teams Considering Competency Criteria Based on Industry 4.0 and Post-COVID-19 Era

The selection of human assets for teams significantly impacts the success and profitability of projects. Industrial Revolution 4.0 (4IR) and the post-COVID-19 conditions impose requirements on virtual collaboration, bots-human collaboration, and teleworking projects. The matrix-structured organization faces challenges in this process because it requires weighing various criteria from distinct perspectives. Accordingly, an inappropriate team selection process can result in high costs or failure. Team member competency criteria are identified based on 4IR, in this study. The study also evaluates the theory of generations based on the fact that project teams consist of members from different generations, each with unique characteristics. To this end, a multi-objective allocation model is presented that maximizes competency level while minimizing costs, considering the organizational structure, the 4IR, the post-COVID-19 era condition, and the generation theory. The study attempts to provide decision-makers in multiple-project organizations with a realistic picture to make a trade-off between the cost and competency level of teams. The linear best-worst method (BWM) is used to weigh the competency criteria. Regarding the developed bi-objective model, the Augmented ε- Constraint (AUGMECON) method is utilized to solve the problem. The model is also validated using the Iran Mall project. The findings indicate that younger generations have almost 1.3 more competence scores in virtual communication than older generations. Also, the organization should increase expenditures by 7.1% to reach the highest level of competency.