.png){kind=icon}
返回
A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2021.107099.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, an efficient decomposition-based multi-objective genetic programming hyper-heuristic (MOGP-HH/D) approach is proposed for the multi-skill resource constrained project scheduling problem (MS-RCPSP) with the objectives of minimizing the makespan and the total cost simultaneously. First, the decomposition mechanism is presented to improve the diversity of solutions. Second, a single-list encoding scheme and an improved repair-based decoding scheme are designed to represent individuals and construct feasible schedules, respectively. Third, ten adaptive heuristics are developed elaborately to constitute a list of low-level heuristics (LLHs). Fourth, genetic programming is employed as the high-level heuristic (HLH) to generate a promising heuristics sequence from the LLHs set flexibly. Finally, the Taguchi method of design-of-experiment (DOE) is conducted to analyze the performance of parameter settings. The effectiveness of MOGP-HH/D is evaluated on a typical benchmark dataset and computational results exhibit the superiority of the proposed algorithm over the existing methods in solving multi-objective MS-RCPSP. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Decomposition
Multi-objective
Genetic programming
Hyper-heuristic
Resource constrained scheduling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Teaching-learning-based optimization algorithm for multi-skill resource constrained project scheduling problem.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
A hybrid genetic algorithm for the resource-constrained project scheduling problem一种求解资源受限项目调度问题的混合遗传算法
PREAI
分享收藏
查看更多内容