A deep reinforcement learning approach for chemical production scheduling

Hubbs, Christian D.; Li, Can; Sahinidis, Nikolaos V.; Grossmann, Ignacio E.; Wassick, John M.

doi:10.1016/j.compchemeng.2020.106982

Cited by 116 publications

(58 citation statements)

References 23 publications

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“…A current focus in RL research is centered around creating systems that learn more efficiently with remarkable recent advances in causal discovery (Zhu et al, 2019) and meta-learning (Co-Reyes et al, 2021). Looking ahead, the RL framework holds the potential to improve solutions across many complex chemical engineering problems, such as scheduling (Hubbs et al, 2020), control (Shin et al, 2019), and process optimization (Petsagkourakis et al, 2020). The framework is also a viable alternative to exact optimization-based approaches for large design spaces.…”

Section: Optimization Platforms For Integrated Multiscale Designmentioning

confidence: 99%

Paradigm Shift: The Promise of Deep Learning in Molecular Systems Engineering and Design

Alshehri

You

2021

Front. Chem. Eng.

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The application of deep learning to a diverse array of research problems has accelerated progress across many fields, bringing conventional paradigms to a new intelligent era. Just as the roles of instrumentation in the old chemical revolutions, we reinforce the necessity for integrating deep learning in molecular systems engineering and design as a transformative catalyst towards the next chemical revolution. To meet such research needs, we summarize advances and progress across several key elements of molecular systems: molecular representation, property estimation, representation learning, and synthesis planning. We further spotlight recent advances and promising directions for several deep learning architectures, methods, and optimization platforms. Our perspective is of interest to both computational and experimental researchers as it aims to chart a path forward for cross-disciplinary collaborations on synthesizing knowledge from available chemical data and guiding experimental efforts.

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Section: Optimization Platforms For Integrated Multiscale Designmentioning

confidence: 99%

Paradigm Shift: The Promise of Deep Learning in Molecular Systems Engineering and Design

Alshehri

You

2021

Front. Chem. Eng.

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“…Deep RL has been spotlighted after demonstrating an extraordinary performance for problems and applied scheduling problems in a variety of industries [20][21][22]. Among the algorithms for deep RL, the deep Q-network (DQN) has been employed by many researchers.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

2020

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In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q-network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

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“…Equation 3shows the max-min problem that maximizes the minimum data-rate of any user, and Equations (4)- (6) show the different constraints to ensure QoS and successful data transmission. The constraints in the above equations indicate the following: 1) The constraint in (4) is necessary for the successful application of the SIC method.…”

Section: System Model and Optimization Problemmentioning

confidence: 99%

“…In the recent past, the deep reinforcement learning (DRL) approach has successfully been employed to solve optimization problems in many fields of engineering such as non-convex and non-deterministic polynomial-time (NP)hard optimization problems in wireless communications [5], [6], [7], [8], [2]. Non-orthogonal multiple access technique (NOMA) is an innovative multiple-access method proposed for 5G and beyond networks [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Based Power Allocation for the Max-Min Optimization in Non-Orthogonal Multiple Access

2020

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A deep reinforcement learning approach for chemical production scheduling

Cited by 116 publications

References 23 publications

Paradigm Shift: The Promise of Deep Learning in Molecular Systems Engineering and Design

Paradigm Shift: The Promise of Deep Learning in Molecular Systems Engineering and Design

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

Deep Reinforcement Based Power Allocation for the Max-Min Optimization in Non-Orthogonal Multiple Access

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