Decision Support for Adaptive Human Capital Management: A Machine Learning & Mathematical Programming Framework for Skill-Based Recruitment
Ayelet Dabush, M.Sc. student in the School of Industrial & Intelligent Systems Engineering
Advisors:
Prof. Yossi Bukchin, School of Industrial & Intelligent Systems Engineering, Tel-Aviv University, Israel,
Prof. Hila Chalutz-Ben Gal, The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Israel
Yariv Tabac, The Extreme Group - HR Tech Company
Abstract:
The nature of work is transforming driven by rapid technological change and the spread of artificial intelligence (AI) and automation. AI is reshaping the labor market and organizational processes by augmenting human skills, automating routine tasks, and enabling new modes of collaboration between humans and intelligent systems. These rapid changes enable greater workforce flexibility, demands new skill sets-both technical and soft skills-and accelerates the transition from traditional one-to-one (candidate-per-job) employment models, to more dynamic, many-to-many skill-based projects. The integration of AI in human resource functions further emphasizes the need for scaling the human capital assessment processes with robust decision support tools to identify required technical and soft skills.
This paper addresses this pivotal shift in employee recruitment toward data-driven, skill-based allocation frameworks that factor both technical and soft skills. The study introduces a novelle analytical framework for skill-based recruitment that combines machine learning and mathematical programming tools. We train multiple classification models on a large dataset of recruiter-labelled historical data, extracting feature embeddings from both candidate résumés and job descriptions. The resulting probability scores are then integrated in mixed-integer optimization models to solve large-scale assignment problems under real-world operational constraints. The experimental design includes over 150 simulation iterations per scenario to evaluate allocation performance and sensitivity across defined hypotheses. Beyond conventional one-to-one integer assignment (candidate-to-job), we operationalize a novel fractional assignment model (candidate-to-project) allowing candidates to partially fulfil multiple project needs, thus reflecting emerging managerial need for flexibility, skill-based and team-level configuration.
Research findings reveal that transitioning from recruiter-based to skill-based allocation is feasible, as the proposed skill-based model achieves comparable performance across multiple parameters. By comparing the integer and fractional allocation results, the study also demonstrates the boundaries, trade-offs, and practical value of prescriptive, fractional skill-based recruitment strategy for improved workforce management, particularly when technical skills or candidate pool size are high. These results advance the literature by providing a validated framework for optimizing human capital and talent allocation in the rapidly shifting labor market
Bio:
Ayelet Gvili (Dabush) is a Master's student in Industrial Engineering, specializing in Data Science. She holds a Bachelor of Science degree in Industrial Engineering from Tel Aviv University. Ayelet currently works as a Data Scientist at Menora Mivtachim, a leading insurance and financial services company, where she applies data analysis and machine learning techniques to support data-driven decision-making and process optimization.
