On the public citation graph

Deep Reinforcement Learning for Engineering Project Management: A Systematic Review

0 signed · 35 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00170-021-07784-yopenalex-snapshot
Cutting tool prognostics enabled by hybrid CNN-LSTM with transfer learning
inferredcites →https://doi.org/10.1007/s10845-022-01915-2openalex-snapshot
On reliability of reinforcement learning based production scheduling systems: a comparative survey
inferredcites →https://doi.org/10.1007/s13042-020-01155-xopenalex-snapshot
A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction
inferredcites →https://doi.org/10.1007/s40070-013-0020-7openalex-snapshot
Multi-time-scale Markov decision processes for organizational decision-making
inferredcites →https://doi.org/10.1016/j.cirpj.2022.11.003openalex-snapshot
Deep reinforcement learning in smart manufacturing: A review and prospects
inferredcites →https://doi.org/10.1016/j.compind.2022.103748openalex-snapshot
Reinforcement learning for industrial process control: A case study in flatness control in steel industry
inferredcites →https://doi.org/10.1016/j.eng.2022.05.017openalex-snapshot
Toward Human-in-the-Loop AI: Enhancing Deep Reinforcement Learning via Real-Time Human Guidance for Autonomous Driving
inferredcites →https://doi.org/10.1016/j.fmre.2024.08.002openalex-snapshot
Supply chain resilience: A review from the inventory management perspective
inferredcites →https://doi.org/10.1016/j.ifacol.2023.10.814openalex-snapshot
Multi-objective Reconfigurable Manufacturing System Scheduling Optimisation: A Deep Reinforcement Learning Approach
inferredcites →https://doi.org/10.1016/j.ijproman.2015.09.002openalex-snapshot
Prediction of outcome of construction dispute claims using multilayer perceptron neural network model
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1080/00207543.2021.1973138openalex-snapshot
Deep reinforcement learning in production systems: a systematic literature review
inferredcites →https://doi.org/10.1080/00207543.2022.2058432openalex-snapshot
Deep reinforcement learning for dynamic scheduling of a flexible job shop
inferredcites →https://doi.org/10.1080/00207543.2022.2069525openalex-snapshot
The ASSISTANT project: AI for high level decisions in manufacturing
inferredcites →https://doi.org/10.1080/00207543.2023.2233641openalex-snapshot
A deep reinforcement learning based hyper-heuristic for modular production control
inferredcites →https://doi.org/10.1109/icsme58846.2023.00015openalex-snapshot
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
inferredcites →https://doi.org/10.1109/jiot.2024.3363610openalex-snapshot
A Remaining Useful Life Prediction Method of Rolling Bearings Based on Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/tcyb.2020.2977661openalex-snapshot
Deep Reinforcement Learning for Multiobjective Optimization
inferredcites →https://doi.org/10.1109/tpami.2023.3292075openalex-snapshot
Transfer Learning in Deep Reinforcement Learning: A Survey
inferredcites →https://doi.org/10.1111/j.1540-5885.2008.00296.xopenalex-snapshot
Perspective: The Stage‐Gate® Idea‐to‐Launch Process—Update, What's New, and NexGen Systems*
inferredcites →https://doi.org/10.1145/3477132.3483588openalex-snapshot
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP
inferredcites →https://doi.org/10.1145/3616864openalex-snapshot
Explainable Reinforcement Learning: A Survey and Comparative Review
inferredcites →https://doi.org/10.1145/3637528.3672052openalex-snapshot
DyPS: Dynamic Parameter Sharing in Multi-Agent Reinforcement Learning for Spatio-Temporal Resource Allocation
inferredcites →https://doi.org/10.1201/9780203913246openalex-snapshot
Effective Opportunity Management for Projects
inferredcites →https://doi.org/10.13166/jms/186050openalex-snapshot
Implementation of value-oriented project portfolio management for the development of complex system
inferredcites →https://doi.org/10.14569/ijacsa.2025.0160661openalex-snapshot
Leveraging LSTM-Driven Predictive Analytics for Resource Allocation and Cost Efficiency Optimization in Project Management
inferredcites →https://doi.org/10.15611/ie.2022.1.05openalex-snapshot
Utilization of Deep Reinforcement Learning for Discrete Resource Allocation Problem in Project Management – a Simulation Experiment
inferredcites →https://doi.org/10.1609/aaai.v39i18.34068openalex-snapshot
Pareto Set Learning for Multi-Objective Reinforcement Learning
inferredcites →https://doi.org/10.1613/jair.2078openalex-snapshot
Anytime Point-Based Approximations for Large POMDPs
inferredcites →https://doi.org/10.2478/mspe-2023-0017openalex-snapshot
Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research
inferredcites →https://doi.org/10.30574/wjarr.2025.28.2.3690openalex-snapshot
Reinforcement Learning-Based Risk Optimization: Automating Strategic Responses in Uncertain Business Landscapes
inferredcites →https://doi.org/10.3390/a16080395openalex-snapshot
Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach
inferredcites →https://doi.org/10.5267/j.jpm.2024.12.002openalex-snapshot
Resource-constrained project scheduling problem: Review of recent developments
inferredcites →https://doi.org/10.5772/intechopen.103984openalex-snapshot
Deep Learning and Reinforcement Learning
inferredcites →https://doi.org/10.58729/1941-6679.1558openalex-snapshot
The Applications of Artificial Intelligence in Managing Project Processes and Targets: A Systematic Analysis
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.