Experimental Evaluation of Agent-based Approaches to Solving Multi-mode Resource-Constrained Project Scheduling Problem

ABSTRACT

In this paper, a rank-based nonparametric statistical test for measuring the effect of cooperation between optimization agents solving the multi-mode resource-constrained project scheduling problem is presented. To solve this NP-hard optimization problem, different methods are applied including population- and agent-based approaches. One of them is a team of asynchronous agents composed of multiple optimization agents, management agents, and common memories, which through interactions produce solutions of hard optimization problems. Optimization agents represent different methods including local search, path relinking, or tabu search. Interactions are managed through various cooperation strategies based on applying heuristics, reinforcement learning, or population learning.

KEYWORDS:

• Agent
• A-team
• MRCPSP
• multi-agent system
• multi-mode resource-constrained project scheduling problem
• optimization