Distributed Research Project Scheduling Based on Multi-Agent Methods

Abstract

Different project planning and scheduling approaches have been developed. The Operational Research (OR) provides two major planning techniques: CPM (Critical Path Method) and PERT (Program Evaluation and Review Technique). Due to projects complexity and difficulty to use classical methods, new approaches were developed. Artificial Intelligence (AI) initially promoted the automatic planner concept, but model-based planning and scheduling methods emerged later on. The paper adresses the project scheduling optimization problem, when projects are seen as Complex Adaptive Systems (CAS). Taken into consideration two different approaches for project scheduling optimization: TCPSP (Time- Constrained Project Scheduling) and RCPSP (Resource-Constrained Project Scheduling), the paper focuses on a multiagent implementation in MATLAB for TCSP. Using the research project as a case study, the paper includes a comparison between two multi-agent methods: Genetic Algorithm (GA) and Ant Colony Algorithm (ACO).

Keywords

• Project Scheduling
• Multi-agent Method
• Genetic Algorithm
• Swarm Intelligence
• Research Project Management