A Mathematical Method for Optimized Decision-Making and Performance Improvement Through Training and Employee Reallocation Under Resistance to Change

The decrease in employee performance that occurs during organizational change is one of the main problems that this study attempts to address. This phenomenon, which is known as resistance to change, has been directly linked to the failure or abandonment of change initiatives when performance drops to critical levels. This study proposes an innovative approach to organizational change management based on a model that integrates real-time performance monitoring and employee reassignment to tasks. This approach contributes to improving overall system performance and stabilizing costs by achieving a reduction in resistance to change through staff training and dynamic reallocation of human resources. The method utilizes Evolutionary Dynamic Multi-Objective Optimization with the aim of both maximizing performance and minimizing costs. It incorporates the performance of employees in each task and the associated costs, enabling continuous adjustment of task assignments in accordance with temporal variability in the factors that affect the success of organizational change. Experimental simulations show that the proposed method leads to a considerable enhancement in overall system performance, cost stabilization, and a significant reduction in the risk of change abandonment. More specifically, the proposed method demonstrates an improvement in total performance from 55% to over 200% in comparison to three reference methods. Furthermore, it achieves faster recovery and a lower performance drop, especially in critical stages, providing optimized decision-making during the change process and leading to the new desired and improved state being achieved in a time that is up to 27% shorter, consequently reducing the risk of abandonment. The proposed method operates as both an optimization tool and a real-time decision support system. The continuous analysis of employee performance and cost provides actionable indications of the current state of change, allowing for timely detection and intervention.