Solving Domain-Independent Dynamic Programming Problems with Anytime Heuristic Search

Ryo Kuroiwa, J. Christopher Beck
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引用次数: 3

Abstract

Domain-independent dynamic programming (DIDP) is a recently proposed model-based paradigm for combinatorial optimization where a problem is formulated as dynamic programming (DP) and solved by a generic solver. In this paper, we develop anytime heuristic search solvers for DIDP, which quickly find a feasible solution and continuously improve it to prove optimality. We implement six anytime heuristic search algorithms previously used as problem-specific methods and evaluate them on nine different problem classes. Our experimental results show that most of the anytime DIDP solvers outperform an existing A*-based solver, mixed-integer programming, and constraint programming in proving optimality, solution quality, and primal integral across multiple problem classes. In particular, complete anytime beam search (CABS) performs the best, improving on the best-known solution for one instance of traveling salesman problem with time windows and closing five instances of one-to-one multi-commodity pick-and-delivery traveling salesman problems.
用随时启发式搜索求解域无关动态规划问题
领域无关动态规划(DIDP)是最近提出的一种基于模型的组合优化范式,它将问题表述为动态规划(DP),并通过泛型求解器进行求解。在本文中,我们开发了DIDP的随时启发式搜索解,它可以快速找到可行解并不断改进以证明其最优性。我们实现了六种随时启发式搜索算法,这些算法以前用作特定于问题的方法,并在九个不同的问题类别上对它们进行了评估。我们的实验结果表明,大多数任意时间DIDP求解器在证明跨多个问题类的最优性、解质量和原积分方面优于现有的基于A*的求解器、混合整数规划和约束规划。特别是,完全任意时间束搜索(CABS)表现最好,改进了最著名的一个带时间窗口的旅行推销员问题的解决方案,并关闭了五个一对一多商品提货旅行推销员问题的实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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