Item Infomation
Title: | A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups |
Authors: | Funing, Li Sebastian, Lang Bingyuan, Hong |
Issue Date: | 2023 |
Publisher: | Springer |
Abstract: | As an essential scheduling problem with several practical applications, the parallel machine scheduling problem (PMSP) with family setups constraints is difficult to solve and proven to be NP-hard. To this end, we present a deep reinforcement learning (DRL) approach to solve a PMSP considering family setups, aiming at minimizing the total tardiness. The PMSP is first modeled as a Markov decision process, where we design a novel variable-length representation of states and actions, so that the DRL agent can calculate a comprehensive priority for each job at each decision time point and then select the next job directly according to these priorities. Meanwhile, the variable-length state matrix and action vector enable the trained agent to solve instances of any scales. To handle the variable-length sequence and simultaneously ensure the calculated priority is a global priority among all jobs, we employ a rec |
Description: | CC BY |
URI: | https://link.springer.com/article/10.1007/s10845-023-02094-4 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8455 |
Appears in Collections | OER - Kinh tế và Quản lý |
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