learning to repair plans and schedules using a relational (deictic) representation
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ID: 260194
2010
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Abstract
Unplanned and abnormal events may have a significant impact on the feasibility of plans and schedules which requires to repair them 'on-the-fly' to guarantee due date compliance of orders-in-progress and negotiating delivery conditions for new orders. In this work, a repair-based rescheduling approach based on the integration of intensive simulations with logical and relational reinforcement learning is proposed. Based on a relational (deictic) representation of schedule states, a number of repair operators have been designed to guide the search towards a goal state. The knowledge generated via simulation is encoded in a relational regression tree for the Q-value function defining the utility of applying a given repair operator at a given schedule state. A prototype implementation in Prolog language is discussed using a representative example of three batch extruders processing orders for four different products. The learning curve for the problem of inserting a new order vividly illustrates the advantages of logical and relational learning in rescheduling.
| Reference Key |
palombarini2010brazilianlearning
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|---|---|
| Authors | ;J. Palombarini;E. Martínez |
| Journal | database and network journal |
| Year | 2010 |
| DOI |
10.1590/S0104-66322010000300006
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| URL | |
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