info:eu-repo/semantics/article
Real-time rescheduling of production systems using relational reinforcement learning
Fecha
2011-12Registro en:
Palombarini, Jorge Andrés; Martínez, Ernesto Carlos; Real-time rescheduling of production systems using relational reinforcement learning; QUALIS CAPES (UFSC); Iberoamerican Journal of Industrial Engineering; 3; 2; 12-2011; 136-153
2175-8018
CONICET Digital
CONICET
Autor
Palombarini, Jorge Andrés
Martínez, Ernesto Carlos
Resumen
Most scheduling methodologies developed until now have laid down good theoretical foundations, but there is still the need for real-time rescheduling methods that can work effectively in disruption management. In this work, a novel approach for automatic generation of rescheduling knowledge using Relational Reinforcement Learning (RRL) is presented. Relational representations of schedule states and repair operators enable to encode in a compact way and use in real-time rescheduling knowledge learned through intensive simulations of state transitions. An industrial example where a current schedule must be repaired following the arrival of a new order is discussed using a prototype application – SmartGantt®- for interactive rescheduling in a reactive way. SmartGantt® demonstrates the advantages of resorting to RRL and abstract states for real-time rescheduling. A small number of training episodes are required to define a repair policy which can handle on the fly events such as order insertion, resource break-down, raw material delay or shortage and rush order arrivals using a sequence of operators to achieve a selected goal.