期刊
INFORMATION SCIENCES
卷 373, 期 -, 页码 476-498出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.09.010
关键词
Metaheuristics; Hybrid heuristics; Hyper-heuristics; Monte Carlo tree search; Permutation based local search; Multi-project scheduling
资金
- EPSRC [EP/F033214/1, EP/H000968/1]
- EPSRC [EP/H000968/1, EP/F033214/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/H000968/1, EP/F033214/1] Funding Source: researchfish
Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of hidden instances, i.e. instances not available at the algorithm design phase. (C) 2016 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据