A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

Naimi, Rami; Nouiri, Maroua; Cardin, Olivier

doi:10.3390/su132313016

Cited by 8 publications

(1 citation statement)

References 44 publications

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“…The multiobjective FJSPs have been seldom addressed (Lang et al, 2020;Luo et al, 2021). Furthermore, among the few studies addressing the DRL-based multi-objective FJSP, even fewer studies cared about environmental objectives (Naimi et al, 2021;Du et al, 2022). Therefore, the development of DRL-based methods for solving FJSP is still in the initial stage and not yet systematic (Luo, 2020;Feng et al, 2021;Liu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

CEA-FJSP: Carbon emission-aware flexible job-shop scheduling based on deep reinforcement learning

Wang

Tang

et al. 2022

Front. Environ. Sci.

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Currently, excessive carbon emission is causing visible damage to the ecosystem and will lead to long-term environmental degradation in the future. The manufacturing industry is one of the main contributors to the carbon emission problem. Therefore, the reduction of carbon emissions should be considered at all levels of production activities. In this paper, the carbon emission as a parvenu indicator is considered parallelly with the nobleman indicator, makespan, in the flexible job-shop scheduling problem. Firstly, the carbon emission is modeled based on the energy consumption of machine operation and the coolant treatment during the production process. Then, a deep reinforcement learning-based scheduling model is proposed to handle the carbon emission-aware flexible job-shop scheduling problem. The proposed model treats scheduling as a Markov decision process, where the scheduling agent and the scheduling environment interact repeatedly via states, actions, and rewards. Next, a deep neural network is employed to parameterize the scheduling policy. Then, the proximal policy optimization algorithm is conducted to drive the deep neural network to learn the objective-oriented optimal mapping from the states to the actions. The experimental results verify that the proposed deep reinforcement learning-based scheduling model has prominent optimization and generalization abilities. Moreover, the proposed model presents a nonlinear optimization effect over the weight combinations.

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Section: Introductionmentioning

confidence: 99%

CEA-FJSP: Carbon emission-aware flexible job-shop scheduling based on deep reinforcement learning

Wang

Tang

et al. 2022

Front. Environ. Sci.

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A Hybrid Meta-Heuristic to Solve Flexible Job Shop Scheduling Problem

Zaidi,

Amirat,

Jarboui

et al. 2024

Unsupervised and Semi-Supervised Learning

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Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

Wicaksono,

Trat,

Bashyal

et al. 2024

Int J Adv Manuf Technol

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The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.

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A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

Cited by 8 publications

References 44 publications

CEA-FJSP: Carbon emission-aware flexible job-shop scheduling based on deep reinforcement learning

CEA-FJSP: Carbon emission-aware flexible job-shop scheduling based on deep reinforcement learning

A Hybrid Meta-Heuristic to Solve Flexible Job Shop Scheduling Problem

Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

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