Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling

Thomas Philip Runarsson (1) , Marc Schoenauer (2, 3) , Michèle Sebag (3)

1 School of Engineering and Natural Sciences
2 TAO - Machine Learning and Optimisation
3 LRI - Laboratoire de Recherche en Informatique

Greedy heuristics may be attuned by looking ahead for each possible choice, in an approach called the rollout or Pilot method. These methods may be seen as meta-heuristics that can enhance (any) heuristic solution, by repetitively modifying a master solution: similarly to what is done in game tree search, better choices are identified using lookahead, based on solutions obtained by repeatedly using a greedy heuristic. This paper first illustrates how the Pilot method improves upon some simple well known dispatch heuristics for the job-shop scheduling problem. The Pilot method is then shown to be a special case of the more recent Monte Carlo Tree Search (MCTS) methods: Unlike the Pilot method, MCTS methods use random completion of partial solutions to identify promising branches of the tree. The Pilot method and a simple version of MCTS, using the $\varepsilon$-greedy exploration paradigms, are then compared within the same framework, consisting of 300 scheduling problems of varying sizes with fixed-budget of rollouts. Results demonstrate that MCTS reaches better or same results as the Pilot methods in this context.

Domaines: Intelligence artificielle [cs.AI]

Thomas Philip Runarsson, Marc Schoenauer, Michèle Sebag. Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling. Learning and Intelligent OptimizatioN (LION'6), Jan 2012, Paris, France. pp.408-423. ⟨hal-00736968⟩