A Genetic Programming-Based Iterative Approach for the Integrated Process Planning and Scheduling Problem
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- @Article{Xuedong_Zhu:ASE,
-
author = "Xuedong Zhu and Xinxing Guo and Weihao Wang and
Jianguo Wu",
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title = "A Genetic Programming-Based Iterative Approach for the
Integrated Process Planning and Scheduling Problem",
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journal = "IEEE Transactions on Automation Science and
Engineering",
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year = "2022",
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volume = "19",
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number = "3",
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pages = "2566--2580",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1558-3783",
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DOI = "doi:10.1109/TASE.2021.3091610",
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abstract = "The integrated process planning and scheduling (IPPS)
problem is studied in this article, in which operation
sequencing, process plan selection, and machine
selection are decided simultaneously. For different
scenarios, three mixed-integer linear programming
(MILP) models are designed. Then, in view of the
workload of machines and processing times of jobs, two
machine selection techniques are introduced to simplify
the optimization of these MILP models. By exploring the
structural properties of the MILP models, we put
forward a novel lower bound to act as a measurement for
the performance of the related algorithms. Considering
the real-time requirement and complexity of instances
in practice, we design a hybrid greedy heuristic based
on a new decision structure of the problem and
dispatching rules. Furthermore, in order to create
effective dispatching rules to improve the hybrid
greedy heuristic, enhanced genetic programming
(GP)-based iterative approach is proposed. Experimental
results indicate that our approaches are better than
other available approaches for the IPPS problem and can
reduce the computational time while providing
high-quality solutions.",
-
notes = "Also known as \cite{9475992}",
- }
Genetic Programming entries for
Xuedong Zhu
Xinxing Guo
Weihao Wang
Jianguo Wu
Citations