Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{LI:2022:RCM,
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author = "Yuxin Li and Wenbin Gu and Minghai Yuan and
Yaming Tang",
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title = "Real-time data-driven dynamic scheduling for flexible
job shop with insufficient transportation resources
using hybrid deep {Q} network",
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journal = "Robotics and Computer-Integrated Manufacturing",
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volume = "74",
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pages = "102283",
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year = "2022",
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ISSN = "0736-5845",
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DOI = "doi:10.1016/j.rcim.2021.102283",
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URL = "https://www.sciencedirect.com/science/article/pii/S0736584521001630",
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keywords = "genetic algorithms, genetic programming, Flexible job
shop scheduling, Insufficient transportation resources,
Hybrid deep Q network, Multiobjective optimization,
Dynamic scheduling",
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abstract = "With the extensive application of automated guided
vehicles in manufacturing system, production scheduling
considering limited transportation resources becomes a
difficult problem. At the same time, the real
manufacturing system is prone to various disturbance
events, which increase the complexity and uncertainty
of shop floor. To this end, this paper addresses the
dynamic flexible job shop scheduling problem with
insufficient transportation resources (DFJSP-ITR) to
minimize the makespan and total energy consumption. As
a sequential decision-making problem, DFJSP-ITR can be
modeled as a Markov decision process where the agent
should determine the scheduling object and allocation
of resources at each decision point. So this paper
adopts deep reinforcement learning to solve DFJSP-ITR.
In this paper, the multiobjective optimization model of
DFJSP-ITR is established. Then, in order to make agent
learn to choose the appropriate rule based on the
production state at each decision point, a hybrid deep
Q network (HDQN) is developed for this problem, which
combines deep Q network with three extensions.
Moreover, the shop floor state model is established at
first, and then the decision point, generic state
features, genetic-programming-based action space and
reward function are designed. Based on these contents,
the training method using HDQN and the strategy for
facing new job insertions and machine breakdowns are
proposed. Finally, comprehensive experiments are
conducted, and the results show that HDQN has
superiority and generality compared with current
optimization-based approaches, and can effectively deal
with disturbance events and unseen situations through
learning",
- }
Genetic Programming entries for
Yuxin Li
Wenbin Gu
Minghai Yuan
Yaming Tang
Citations