Evolving parameterised policies for stochastic constraint programming

Authors:
Steven Prestwich;S. Armagan Tarim;Roberto Rossi;Brahim Hnich
Affiliations:
Cork Constraint Computation Centre, University College Cork, Ireland;Operations Management Division, Nottingham University Business School, Nottingham, UK;Logistics, Decision and Information Sciences Group, Wageningen UR, The Netherlands;Faculty of Computer Science, Izmir University of Economics, Turkey
Venue:
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Year:
2009

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Abstract

Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several solution methods have been proposed but none seems practical for large multi-stage problems. We propose an incomplete approach: specifying a policy tree indirectly by a parameterised function, whose parameter values are found by evolutionary search. On some problems this method is orders of magnitude faster than a state-of-the-art scenario-based approach, and it also provides a very compact representation of policy trees.