Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem Academic Article uri icon

abstract

  • A two-robot flow-shop scheduling problem with n identical jobs and m machines is defined and evaluated for four robot collaboration levels corresponding to different levels of information sharing, learning and assessment: Full–robots work together, performing self and joint learning sharing full information; Pull–one robot decides when and if to learn from the other robot; Push–one robot may force the second to learn from it and None–each robot learns independently with no information sharing. Robots operate on parallel tracks, transporting jobs between successive machines, returning empty to a machine to move another job. The objective is to obtain a robot schedule that minimises makespan (C max) for machines with varying processing times. A new reinforcement learning algorithm is developed, using dual Q-learning functions. A novel feature in the collaborative algorithm …

publication date

  • February 16, 2016