Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach

Area of Science:

• Software Engineering
• Operations Research
• Artificial Intelligence

Background:

• Resource allocation in software projects is complex due to dynamic factors like engineer skill levels and project changes.
• Frequent rescheduling is common, necessitating robust decision-making tools for project managers.
• Existing methods may not adequately balance schedule efficiency with stability, leading to increased costs.

Purpose of the Study:

• To propose an effective and efficient software project staffing model.
• To incorporate dynamic elements of staff productivity into resource allocation.
• To develop rescheduling strategies that consider both efficiency and stability.

Main Methods:

• A software project staffing model incorporating dynamic staff productivity.
• Optimization using a Genetic Algorithm (GA) and Hill Climbing (HC).
• Rescheduling strategies designed to minimize shifting cost increases while maintaining efficiency.

Main Results:

• The proposed model demonstrates effectiveness in real-world case studies and simulations.
• The method achieves performance comparable to other heuristic algorithms in most scenarios.
• The approach successfully balances schedule efficiency and stability during rescheduling.

Conclusions:

• The developed model provides a valuable tool for project managers facing dynamic staffing challenges.
• The GA and HC-based optimizer effectively handles dynamic staff productivity and rescheduling.
• The proposed strategy offers a practical solution for optimizing software project resource allocation.