Abstract
This paper discussed the multi-projects scheduling problem in Cloud Manufacturing system, where each of the projects is a set of interrelated tasks, and these projects need to be scheduled timely and carefully. However, scheduling massive projects can be challenging due to the uneven distribution of the services and the uncertain arrival of projects. Therefore, we (1) established a dual-objectives optimisation model to minimise both the total makespan and the logistical distance; (2) proposed a Reinforcement Learning based Assigning Policy (RLAP) approach to obtain non-dominated solution set; (3) designed a dynamic state representing an algorithm for agents to determine their decision environment when using RLAP. Experiment results show that RLAP can adjust the distribution of service load according to the nearby tasks, and the schedule quality is improved by and compared with NSGA-II and Q-learning, respectively. Besides, the RLAP method has the ability to schedule stochastically arriving projects.
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