MultiAgent Reinforcement Learning Tool for Job Shop Scheduling Problems The emergence of Industry 4.0 allows for new approaches to solve industrial problems such as the Job Shop Scheduling Problem. It has been demonstrated that MultiAgent Reinforcement Learning approaches are highly promising to handle complex scheduling scenarios. In this work we propose a user friendly MultiAgent Reinforcement Learning tool, more appealing for industry. It allows the users to interact with the learning algorithms in such a way that all the constraints in the production floor are carefully included and the objectives can be adapted to real world scenarios. The user can either keep the best schedule obtained by a QLearning algorithm or adjust it by fixing some operations in order to meet certain constraints, then the tool will optimize the modified solution respecting the user preferences using two possible alternatives. These alternatives are validated using ORLibrary benchmarks, the experiments show that the modified QLearning algorithm is able to obtain the best results.

A framework for scheduling in cloud manufacturing with deep reinforcement learning Cloud manufacturing is a novel serviceoriented networked manufacturing paradigm that aims to provide ondemand manufacturing cloud services to consumers. Scheduling is a critical means for achieving that aim. Currently, research on scheduling in cloud manufacturing is still in its infancy, and current frequently adopted metaheuristic algorithmbased approaches have some shortcomings, e.g. they require complex design processes and lack adaptability to dynamic environments. Deep reinforcement learning (DRL) that combines advantages of reinforcement learning and deep learning provides an efficient, adaptive and intelligent approach for solving scheduling problems in cloud manufacturing. However, to the best of our knowledge, there has been no application of DRL to scheduling in cloud manufacturing. This work conducts a preliminary exploration over this issue. First, a DRLbased framework for scheduling in cloud manufacturing is proposed. Then a DRL model for online singletask scheduling in cloud manufacturing is presented to demonstrate the effectiveness of the framework. DRL as a promising technique will find wide applications in cloud manufacturing, and this work can provide some reference for future research on this.