Scheduling problems appear on a regular basis in many real life situations, whenever it is necess... more Scheduling problems appear on a regular basis in many real life situations, whenever it is necessary to allocate resources to perform tasks, optimizing one or more objective functions. Depending on the problem being solved, these tasks can take different forms, and the objectives can also vary. This research addresses scheduling in manufacturing environments, where the reports requested by the customers have to be scheduled in a set of machines with capacity constraints. Additionally, there is a set of limitations imposed by the company that must be taken into account when a feasible solution is built. To solve this problem, a general algorithm is proposed, which initially distributes the total capacity of the system among the existing resources, taking into account the capacity of each them, after that, each resource decides in which order it will process the reports assigned to it. The experimental study performed shows that the proposed approach allows to obtain feasible solution...
La secuenciacion de tareas es un area muy amplia en la cual muchos investigadores se han enfocado... more La secuenciacion de tareas es un area muy amplia en la cual muchos investigadores se han enfocado en los ultimos anos. En las empresas generalmente esta planificacion se realiza de forma manual o semiautomatica. En este trabajo se propone la aplicacion de sistemas neuroborrosos en la clasificacion de reportes en problemas de secuenciacion, paso necesario para identificar en que recurso se procesara el reporte y posteriormente conformar la secuencia o planificacion de trabajo del dia. Para la clasificacion de los reportes que arriban al sistema se utilizan cuatro algoritmos neuroborrosos. Los experimentos muestran que el algoritmo que mejores resultados obtiene es IVTURS, y las reglas difusas obtenidas por el mismo son analizadas para arribar a conclusiones sobre la distribucion de reportes entre los recursos.
Scheduling problems are present in many processes that occur in the manufacturing industry, where... more Scheduling problems are present in many processes that occur in the manufacturing industry, where it is necessary to perform a set of operations at certain periods of time and it also needs to allocate limited resources to perform these tasks. Some of these problems occur in online environments, because there is no prior knowledge of the arrival of the jobs or the time it would take for each job to be processed in each of the machines. In this paper we study and propose a solution to online scheduling problems based on an existing case study from the literature, using two Reinforcement Learning algorithms. Besides we extend the study case to two more complex scenarios. The results obtained show the superiority of Q-Learning algorithm over Learning Automata algorithm due to the flexibility of Q-Learning’s parameters. These results were validated using statistical tests where an algorithm is better than other if its difference between the generated and the processed jobs is less. Frie...
The emergence of Industry 4.0 allows for new approaches to solve industrial problems such as the ... more 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 Multi-Agent Reinforcement Learning approaches are highly promising to handle complex scheduling scenarios. In this work we propose a user friendly Multi-Agent 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 Q-Learning 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 OR-Library benchmarks, the experiments show that the modified Q-Learning algorithm is able to obtain the best...
The emergence of Industry 4.0 allows for new approaches to solve industrial problems such as the ... more 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 Multi-Agent Reinforcement Learning approaches are highly promising to handle complex scheduling scenarios. In this work we propose a user friendly Multi-Agent 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 Q-Learning 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 OR-Library benchmarks, the experiments show that the modified Q-Learning algorithm is able to obtain the best...
Scheduling problems appear on a regular basis in many real life situations, whenever it is necess... more Scheduling problems appear on a regular basis in many real life situations, whenever it is necessary to allocate resources to perform tasks, optimizing one or more objective functions. Depending on the problem being solved, these tasks can take different forms, and the objectives can also vary. This research addresses scheduling in manufacturing environments, where it is necessary to sequence the reports requested by customers in a set of machines with capacity constraints. Additionally, there is another set of limitations that must be taken into account when constructing a feasible solution, which means that the system currently used does not efficiently solve the report scheduling problem, taking into account all the constraints of the company. To solve this problem, a general algorithm is proposed, which initially distributes the total capacity of the system among the existing resources, taking into account the capacity presented by teach hem, and following one of four possible s...
Scheduling problems appear on a regular basis in many real life situations, whenever it is necess... more Scheduling problems appear on a regular basis in many real life situations, whenever it is necessary to allocate resources to perform tasks, optimizing one or more objective functions. Depending on the problem being solved, these tasks can take different forms, and the objectives can also vary. This research addresses scheduling in manufacturing environments, where the reports requested by the customers have to be scheduled in a set of machines with capacity constraints. Additionally, there is a set of limitations imposed by the company that must be taken into account when a feasible solution is built. To solve this problem, a general algorithm is proposed, which initially distributes the total capacity of the system among the existing resources, taking into account the capacity of each them, after that, each resource decides in which order it will process the reports assigned to it. The experimental study performed shows that the proposed approach allows to obtain feasible solution...
La secuenciacion de tareas es un area muy amplia en la cual muchos investigadores se han enfocado... more La secuenciacion de tareas es un area muy amplia en la cual muchos investigadores se han enfocado en los ultimos anos. En las empresas generalmente esta planificacion se realiza de forma manual o semiautomatica. En este trabajo se propone la aplicacion de sistemas neuroborrosos en la clasificacion de reportes en problemas de secuenciacion, paso necesario para identificar en que recurso se procesara el reporte y posteriormente conformar la secuencia o planificacion de trabajo del dia. Para la clasificacion de los reportes que arriban al sistema se utilizan cuatro algoritmos neuroborrosos. Los experimentos muestran que el algoritmo que mejores resultados obtiene es IVTURS, y las reglas difusas obtenidas por el mismo son analizadas para arribar a conclusiones sobre la distribucion de reportes entre los recursos.
Scheduling problems are present in many processes that occur in the manufacturing industry, where... more Scheduling problems are present in many processes that occur in the manufacturing industry, where it is necessary to perform a set of operations at certain periods of time and it also needs to allocate limited resources to perform these tasks. Some of these problems occur in online environments, because there is no prior knowledge of the arrival of the jobs or the time it would take for each job to be processed in each of the machines. In this paper we study and propose a solution to online scheduling problems based on an existing case study from the literature, using two Reinforcement Learning algorithms. Besides we extend the study case to two more complex scenarios. The results obtained show the superiority of Q-Learning algorithm over Learning Automata algorithm due to the flexibility of Q-Learning’s parameters. These results were validated using statistical tests where an algorithm is better than other if its difference between the generated and the processed jobs is less. Frie...
The emergence of Industry 4.0 allows for new approaches to solve industrial problems such as the ... more 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 Multi-Agent Reinforcement Learning approaches are highly promising to handle complex scheduling scenarios. In this work we propose a user friendly Multi-Agent 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 Q-Learning 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 OR-Library benchmarks, the experiments show that the modified Q-Learning algorithm is able to obtain the best...
The emergence of Industry 4.0 allows for new approaches to solve industrial problems such as the ... more 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 Multi-Agent Reinforcement Learning approaches are highly promising to handle complex scheduling scenarios. In this work we propose a user friendly Multi-Agent 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 Q-Learning 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 OR-Library benchmarks, the experiments show that the modified Q-Learning algorithm is able to obtain the best...
Scheduling problems appear on a regular basis in many real life situations, whenever it is necess... more Scheduling problems appear on a regular basis in many real life situations, whenever it is necessary to allocate resources to perform tasks, optimizing one or more objective functions. Depending on the problem being solved, these tasks can take different forms, and the objectives can also vary. This research addresses scheduling in manufacturing environments, where it is necessary to sequence the reports requested by customers in a set of machines with capacity constraints. Additionally, there is another set of limitations that must be taken into account when constructing a feasible solution, which means that the system currently used does not efficiently solve the report scheduling problem, taking into account all the constraints of the company. To solve this problem, a general algorithm is proposed, which initially distributes the total capacity of the system among the existing resources, taking into account the capacity presented by teach hem, and following one of four possible s...
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