Adapting a Reinforcement Learning Approach for the Flow Shop Environment with sequence-dependent setup time

The tasks scheduling problem on linear production systems, Flow Shop Scheduling Problems, has been a great importance in the operations research which seeks to establish optimal job scheduling in machines within a production process in an industry in general. The problem considered here is to find a permutation of jobs to be sequentially processed on a number of machines under the restriction that the processing of each job has to be continuous with respect to the objective of minimizing the completion time of all jobs, known in literature as makespan or C max . Furthermore, its considerate setup-time between two jobs. This problem is as NP-hard, it is typical of combinatorial optimization and can be found in manufacturing environments, where there are conventional machines-tools and different types of pieces which share the same route. The following research presents an adaptation of Reinforcement Learning algorithm known as Q-Learning to solve problems of the Flow Shop category. This algorithm is based on learning an action-value function that gives the expected utility of taking a given action in a given state where an agent is associated to each of the resources. Finally, the algorithm is tested with problems of different levels of complexity in order to obtain satisfactory results in terms of solutions quality.