A reinforcement learning based multi-method approach for stochastic resource constrained project scheduling problems

计算机科学 强化学习 数学优化 布谷鸟搜索 调度(生产过程) 人工智能 地铁列车时刻表 机器学习 数学 粒子群优化 操作系统
作者
Karam M. Sallam,Ripon K. Chakrabortty,Michael J. Ryan
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:169: 114479-114479 被引量:36
标识
DOI:10.1016/j.eswa.2020.114479
摘要

The Resource-Constrained Project Scheduling Problem (RCPSP) has been widely accepted as a challenging research topic due to its NP-hard nature. Because of the dynamic nature of real-world problems, stochastic-RCPSPs (SRCPSPs) are also receiving greater attention among researchers. To solve these extended RCPSPs (i.e., SRCPSPs), this paper proposes an reinforcement learning based meta-heuristic switching approach that utilizes the powers of both multi-operator differential evolution (MODE) and discrete cuckoo search (DCS) algorithms in single algorithmic framework. Reinforcement learning (RL) is introduced as a technique to select either MODE or DCS based on the diversity of population and quality of solutions. To deal with uncertain durations, a chance-constrained based approach with some belief degrees is also considered and solved by this proposed RL based multi-method approach (i.e., DECSwRL-CC). Extensive experimentation with benchmark data from the project scheduling library (PSPLIB) demonstrates the efficacy of this proposed multi-method approach. Numerous state of the art chance constrained approaches are taken from the literature to compare the proposed approach and to validate the efficacy of this multi-method approach. This particular strategy is applicable to the risk-averse decision-makers who want to realize the project schedule with a high degree of certainty.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
赘婿应助www采纳,获得10
1秒前
1秒前
ttlash发布了新的文献求助10
1秒前
123fordream完成签到,获得积分10
2秒前
天天快乐应助二十五采纳,获得10
2秒前
2秒前
无辜玉米完成签到,获得积分10
3秒前
酷波er应助舒适的尔容采纳,获得10
4秒前
4秒前
4秒前
4秒前
Li发布了新的文献求助10
4秒前
Amanda完成签到,获得积分10
4秒前
淡然雪枫发布了新的文献求助10
4秒前
5秒前
梦梦发布了新的文献求助10
5秒前
5秒前
只吃饭不洗碗完成签到,获得积分10
5秒前
NexusExplorer应助YU采纳,获得10
5秒前
NexusExplorer应助飞云采纳,获得10
6秒前
LR发布了新的文献求助10
6秒前
多情的正豪完成签到,获得积分10
6秒前
Lucas发布了新的文献求助30
6秒前
6秒前
LLL发布了新的文献求助10
7秒前
亚稳态发布了新的文献求助10
7秒前
7秒前
8秒前
8秒前
123应助daisy采纳,获得10
8秒前
是臻不是真完成签到,获得积分10
8秒前
9秒前
9秒前
SciGPT应助ljs采纳,获得10
9秒前
10秒前
脑洞疼应助hu采纳,获得10
10秒前
aaa发布了新的文献求助30
10秒前
隐形曼青应助爱文字采纳,获得20
10秒前
福同学完成签到,获得积分10
11秒前
徐逊发布了新的文献求助10
11秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969383
求助须知:如何正确求助?哪些是违规求助? 3514211
关于积分的说明 11172730
捐赠科研通 3249476
什么是DOI,文献DOI怎么找? 1794909
邀请新用户注册赠送积分活动 875441
科研通“疑难数据库(出版商)”最低求助积分说明 804827