Swarm intelligence in project management decision sciences: Enhancing resource, allocation, decision making and team collaboration

Abstract

Swarm intelligence (SI) is a paradigm in computing and decision-making processes based on the collective behavior of decentralized agents, including insects, birds, and fish. This study examines the extensive application and development of SI in decision sciences and project management, highlighting its significance in improving future decision-making and collaborative efficiency across multiple sectors, such as logistics, healthcare, urban planning, and project-oriented contexts.

This study employs a mixed-methods approach, combining theoretical and empirical research to gain a comprehensive understanding of SI and its role in modern decision-making scenarios. The methodology is separated into three sections: an appraisal of the literature, a case study, and a comparative analysis. These approaches were used to showcase SI's adaptability and efficacy in addressing complex, dynamic challenges through collective behavior, decentralization, and self-organization principles.

The research emphasizes the effectiveness of SI algorithms, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), in practical applications, enhancing processes and outcomes across various sectors and project environments. The study also examines the real-world applications and theoretical implications of SI, addressing challenges and future prospects for deeper integration into decision-making frameworks, especially in areas such as resource allocation and team collaborations within project management.

The findings reveal that SI not only improves decision-making efficiency but also offers resilient solutions adaptable to evolving conditions. This makes SI a pivotal methodology in advancing decision sciences and elevating project management outcomes. By fostering collaboration and optimizing resource allocation, SI emerges as a transformative tool in contemporary project management, enhancing the discipline's ability to navigate uncertainty and complexity in the future of work and project management domains.