Agent-based scheduling with a learning effect model

Abstract

The learning effect from repeated processing of similar operations or jobs can greatly increase the processing efficiency as knowledge or experiences are gained from the processes of operations or jobs. In this paper, the scheduling problem is modeled and solved by using another domain perspective of the integrated agent-based approach with learning effect. In this approach, an agent-based scheduling environment with a learning effect scheduling agent is proposed, and their modeling and development are also discussed. Learning effect concepts are applied to the environment and its agents, such that the feature of learning effect is included in the model. Throughout the autonomous computation nature of the learning effect scheduling agent in the agent-based scheduling environment, a feasible optimal schedule can be generated according to its algorithms and logical functions so as to minimize the total resource consumption with makespan constraint in the scheduling problem.