Author:

Keywords:

Project scheduling, Multi-agent reinforcement learning, Learning automata, ITEC, Social Sciences, Science & Technology, Technology, Management, Operations Research & Management Science, Business & Economics, project scheduling, multi-agent reinforcement learning, learning automata, RESOURCE CONSTRAINTS, GENETIC ALGORITHM, CLASSIFICATION, PRECEDENCE, SEARCH, 01 Mathematical Sciences, 08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, Operations Research, 35 Commerce, management, tourism and services, 46 Information and computing sciences, 49 Mathematical sciences

Abstract:

Intelligent optimisation refers to the promising technique of integrating learning mechanisms into (meta-)heuristic search. In this paper we use multi-agent reinforcement learning for building high quality solutions for the multi-mode resource-constrained project scheduling problem. We use a network of distributed reinforcement learning agents that cooperate to jointly learn a well performing constructive heuristic. Each agent, being responsible for one activity, uses two simple learning devices, called learning automata, that learn to select a successor activity order and a mode, respectively. By coupling the reward signals for both learning tasks, we can clearly show the advantage of using reinforcement learning in search. We present some comparative results, to show that our method can compete with the best performing algorithms for the multi-mode resource-constrained project scheduling problem, yet using only simple learning schemes without the burden of complex finetuning.