Solving One-Dimensional Cutting Stock Problems with the Deep Reinforcement Learning
نویسندگان
چکیده
It is well known that the one-dimensional cutting stock problem (1DCSP) a combinatorial optimization with nondeterministic polynomial (NP-hard) characteristics. Heuristic and genetic algorithms are two main used to solve (CSP), which has problems of small scale low-efficiency solutions. To better improve stability versatility solution, mathematical model established, objective minimum raw material consumption maximum remaining length. Meanwhile, novel algorithm based on deep reinforcement learning (DRL) proposed in this paper. The consists modules, each designed for different functions. Firstly, pointer network encoder decoder structure as policy utilize underlying mode shared by 1DCSP. Secondly, model-free train parameters optimize sequence. experimental data show (DRL-CSP) can obtain approximate satisfactory solution 82 instances 3 sets very short time, shows good generalization performance practical application potential.
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ژورنال
عنوان ژورنال: Mathematics
سال: 2023
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math11041028