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A review of the flexible job shop scheduling problems considering transportation vehicles
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Bin XIN, Sai LU, Qing WANG, Fang DENG. A review of the flexible job shop scheduling problems considering transportation vehicles[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="A review of the flexible job shop scheduling problems considering transportation vehicles",
author="Bin XIN, Sai LU, Qing WANG, Fang DENG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300795"
}
%0 Journal Article
%T A review of the flexible job shop scheduling problems considering transportation vehicles
%A Bin XIN
%A Sai LU
%A Qing WANG
%A Fang DENG
%J Journal of Zhejiang University SCIENCE C
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%N -1
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%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300795
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T1 - A review of the flexible job shop scheduling problems considering transportation vehicles
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A1 - Qing WANG
A1 - Fang DENG
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
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%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300795
Abstract: The flexible job shop scheduling problem for processing machines and transportation vehicles (FJSP_PT) has garnered significant attention from academia and industry. Due to the inclusion of transportation vehicle scheduling in the scheduling problem of flexible manufacturing systems, solving FJSP_PT becomes more challenging and significantly more practically relevant compared to the flexible job shop scheduling problem. This paper summarizes the assumptions, constraints, objective functions and benchmarks of the FJSP_PT. Then, statistical analysis is conducted on relevant literature up to 2023, including journals, number of articles published each year, and solving algorithms. This paper analyzes recent literature on FJSP_PT, categorizing it based on algorithms into exact algorithms, heuristic algorithms, metaheuristic algorithms, and swarm intelligence-based algorithms. Finally, the research trends and challenges faced by the FJSP_PT problem are summarized.
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