Industrial Load Management using Multi-Agent Reinforcement Learning for Rescheduling

Roesch, Martin; Linder, Christian; Bruckdorfer, Christian; Hohmann, Andrea; Reinhart, Günther

doi:10.1109/ai4i46381.2019.00033

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Wang et al [6] use DRL for dynamic scheduling of jobs for a balanced machine utilization in smart manufacturing. In [7,8], two examples for energy optimization in cyber-physical production systems are presented and, in [9], real-time requirements for JSP fulfilled by a system of heterogeneous agents.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning Multi-Agent System for Resource Allocation in Industrial Internet of Things

Rosenberger

Urlaub

Rauterberg

et al. 2022

Sensors

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The high number of devices with limited computational resources as well as limited communication resources are two characteristics of the Industrial Internet of Things (IIoT). With Industry 4.0 emerges a strong demand for data processing in the edge, constrained primarily by the limited available resources. In industry, deep reinforcement learning (DRL) is increasingly used in robotics, job shop scheduling and supply chain. In this work, DRL is applied for intelligent resource allocation for industrial edge devices. An optimal usage of available resources of the IIoT devices should be achieved. Due to the structure of IIoT systems as well as security aspects, multi-agent systems (MASs) are preferred for decentralized decision-making. In our study, we build a network from physical and virtualized representative IIoT devices. The proposed approach is capable of dealing with several dynamic changes of the target system. Three aspects are considered when evaluating the performance of the MASs: overhead due to the MASs, improvement of the resource usage of the devices as well as latency and error rate. In summary, the agents’ resource usage with respect to traffic, computing resources and time is very low. It was confirmed that the agents not only achieve the desired results in training but also that the learned behavior is transferable to a real system.

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning Multi-Agent System for Resource Allocation in Industrial Internet of Things

Rosenberger

Urlaub

Rauterberg

et al. 2022

Sensors

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“…The applied production system model is based on the approach developed in the preliminary work [44]. A defined number of jobs, which are found in the job queue of every resource in the beginning of a manufacturing shift, is assigned to every production resource, e.g., a manufacturing machine.…”

Section: Production Systemmentioning

confidence: 99%

Smart Grid for Industry Using Multi-Agent Reinforcement Learning

et al. 2020

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The growing share of renewable power generation leads to increasingly fluctuating and generally rising electricity prices. This is a challenge for industrial companies. However, electricity expenses can be reduced by adapting the energy demand of production processes to the volatile prices on the markets. This approach depicts the new paradigm of energy flexibility to reduce electricity costs. At the same time, using electricity self-generation further offers possibilities for decreasing energy costs. In addition, energy flexibility can be gradually increased by on-site power storage, e.g., stationary batteries. As a consequence, both the electricity demand of the manufacturing system and the supply side, including battery storage, self-generation, and the energy market, need to be controlled in a holistic manner, thus resulting in a smart grid solution for industrial sites. This coordination represents a complex optimization problem, which additionally is highly stochastic due to unforeseen events like machine breakdowns, changing prices, or changing energy availability. This paper presents an approach to controlling a complex system of production resources, battery storage, electricity self-supply, and short-term market trading using multi-agent reinforcement learning (MARL). The results of a case study demonstrate that the developed system can outperform the rule-based reactive control strategy (RCS) frequently used. Although the metaheuristic benchmark based on simulated annealing performs better, MARL enables faster reactions because of the significantly lower computation costs for its own execution.

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“…To reduce computational complexity and improve robustness, multi-agent reinforcement learning (MARL) is introduced where energy devices [20] or production resourses [21] regarded as multiple agents can perceive the environment and independently adjust their energy policies to achieve the optimal performance [22]. Different constraints and diversity of energy are the main factors for multiple agents to make decisions in the energy management problem.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

Zhu,

Yang,

Liu

et al. 2022

Preprint

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Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partiallyobservable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process.The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multiagent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring

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Industrial Load Management using Multi-Agent Reinforcement Learning for Rescheduling

Cited by 7 publications

References 5 publications

Deep Reinforcement Learning Multi-Agent System for Resource Allocation in Industrial Internet of Things

Deep Reinforcement Learning Multi-Agent System for Resource Allocation in Industrial Internet of Things

Smart Grid for Industry Using Multi-Agent Reinforcement Learning

Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

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