Real-time rescheduling of production systems using relational reinforcement learning


  • Jorge Palombarini GISIQ - UTN - Fac. Reg.
  • Ernesto Martinez INGAR (CONICET-UTN)


Learning, Rescheduling, Relational Modeling, Agile Manufacturing.


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.   



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Como Citar

Palombarini, J., & Martinez, E. (2012). Real-time rescheduling of production systems using relational reinforcement learning. beroamerican ournal of ndustrial ngineering, 3(6), 136–153. ecuperado de