A prediction model for ranking branch-and-bound procedures for the resource-constrained project scheduling problem

Guo, Weikang; Vanhoucke, Mario; Coelho, José

doi:10.1016/j.ejor.2022.08.042

Cited by 11 publications

(1 citation statement)

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“…In practical scenarios, processes exhibit greater dynamism and susceptibility to interruptions, including but not limited to rush orders, cancellations, alterations in lot sizes, and machinery failures. The academic literature on scheduling has put forth various traditional optimization techniques such as dynamic programming 6 , branch-and-bound 7 , and meta-heuristics 8 to address the dynamic job shop scheduling (DJSS) problem. These approaches cannot deal with unforeseen disruptions.…”

Section: Introductionmentioning

confidence: 99%

Learning dispatching rules via novel genetic programming with feature selection in energy-aware dynamic job-shop scheduling

2023

Sci Rep

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The incorporation of energy conservation measures into production efficiency is widely recognized as a crucial aspect of contemporary industry. This study aims to develop interpretable and high-quality dispatching rules for energy-aware dynamic job shop scheduling (EDJSS). In comparison to the traditional modeling methods, this paper proposes a novel genetic programming with online feature selection mechanism to learn dispatching rules automatically. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives considered energy consumption. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 12.67%, 15.38%, and 11.59% in the meakspan with energy consumption (EMS), mean weighted tardiness with energy consumption (EMWT), and mean flow time with energy consumption (EMFT) scenarios, respectively.

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Section: Introductionmentioning

confidence: 99%