Scheduling projects with multiskill learning effect

Area of Science:

• Operations Research
• Project Management
• Artificial Intelligence

Background:

• Project scheduling is complex, often neglecting the impact of learning on task duration.
• Autonomous learning (skill improvement over time) and induced learning (accelerated improvement through specific actions) are key factors in project performance.

Purpose of the Study:

• To develop a novel project scheduling model that integrates multiskill autonomous and induced learning effects.
• To optimize project makespan and total cost by considering these learning dynamics.

Main Methods:

• A new mathematical model was formulated to represent the project scheduling problem with multiskill learning.
• A genetic algorithm with specialized encoding and decoding was employed for finding optimal solutions.
• A numerical example was utilized to validate the model's efficacy.

Main Results:

• The learning effect significantly impacts project scheduling outcomes and cannot be overlooked.
• The proposed model effectively demonstrates the interplay between learning and project performance.
• Computational results confirm the model's ability to identify optimal schedules.

Conclusions:

• Project managers can leverage the model to balance project duration (makespan) and overall expenses.
• Incorporating induced learning strategies offers a mechanism for cost-benefit analysis in project scheduling.
• The study underscores the necessity of accounting for learning effects in modern project management.