Integrated project scheduling and staff assignment with controllable processing times

Area of Science:

• Operations Research
• Management Science
• Computer Science

Background:

• Project management often involves complex interdependencies between task scheduling and staff allocation.
• Service companies face challenges in optimizing resource allocation due to skill-based task requirements and dynamic task durations.

Purpose of the Study:

• To develop an integrated model for simultaneously scheduling project tasks and assigning skilled personnel.
• To compare controllable processing times with fixed processing times in project management.
• To provide efficient approximate solutions for complex scheduling and assignment problems.

Main Methods:

• Formulation of an integer programming model for integrated task scheduling and staff assignment.
• Development of extensions to the model for diverse operational scenarios.
• Implementation of a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm for approximate solutions.

Main Results:

• The proposed integer programming model effectively captures the complexities of simultaneous scheduling and assignment.
• Controllable processing times offer distinct advantages over fixed processing times in this context.
• The GRASP algorithm yields high-quality approximate solutions within practical time constraints.

Conclusions:

• The integrated approach provides a robust framework for optimizing project task scheduling and staff assignment in service industries.
• The study highlights the benefits of considering variable task durations and skill-based assignments.
• The GRASP algorithm serves as an effective heuristic for solving computationally intensive project management problems.