Improved branch and bound in constraint logic programming

Abstract

Complex multi-stage decision making problems often involve uncertainty, for example, regarding demand or processing times. Stochastic constraint programming was proposed as a way to formulate and solve such decision problems, involving arbitrary constraints over both decision and random variables. What stochastic constraint programming currently lacks is support for the use of factorized probabilistic models that are popular in the graph-ical model community. We show how a state-of-the-art probabilistic inference engine can be integrated into standard constraint solvers. The resulting approach searches over the And-Or search tree directly, and we investigate tight bounds on the expected utility objective. This significantly improves search efficiency and outperforms scenario-based methods that ground out the possible worlds.

Programming Constraint Services

“…Mudambi and Schimpf discuss in [96] distributed search that also relies on recomputation. A refinement of this work addresses branch-and-bound search [112]. Perron briefly sketches parallel search for ILOG Solver in [103].…”

“…Exploration overhead is a consequence of performing branch-and-bound in parallel and is independent of the implementation of the search engines. A different approach to parallel best-solution search is presented by Prestwich and Mudambi in [112]. They use cost-parallelism, where several searches for a solution with different cost bounds are performed in parallel.…”