Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling

Zhang, Fangfang; Mei, Yi; Nguyen, Su; Zhang, Mengjie

doi:10.1007/978-3-031-14721-0_4

Cited by 3 publications

(4 citation statements)

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“…A threshold value ( ) is then computed for penalizing the candidates in subsequent steps (line 4). After the previous initial steps, the survivors are chosen from the candidate set to create the new population through − 1 iterations (see lines [5][6][7][8][9][10][11][12][13][14][15]. The algorithm categorizes the candidates into the penalized set ( ) and the non-penalized set ( ) at each iteration (line 6).…”

Section: A Novel Gp Methodsmentioning

confidence: 99%

“…Moreover, when evaluating the overall performance of each dispatching rules , the fitness function is calculated by Equation (12), where ( , ) is the value of scheduling objective, which is calculated by applying the rule to a training instance ∈ , ( ) denotes the target value, obtained by the reference rule in the same training instance. Due to the impressive results for minimizing the MT, MWT, MFT objectives, the Covert, WATC, PT+WINQ rules are adopted as reference rules, respectively [5,19].…”

Section: Discrete Event Simulation Modelmentioning

confidence: 99%

“…The marks "+", "−", and "=" inside the findings indicate that the associated result is considerably superior to, inferior to, or equal to its counterparts, respectively. The test performance of the evolved rule is defined as the percentage deviation from the reference rule, i.e., 100 • (1 − ( )), where ( ) is estimated by Equation (12). The percentage deviation for the MT, MWT, and MFT targets is shown in Figure 3a-c, respectively.…”

Section: Training Performancementioning

confidence: 99%

“…Therefore, decreasing the number of terminals is one of the ways to reduce the scope of GP search and enhance the search capability. In DJSS, a wide range of features can be included in the terminal sets, such as system-related, job-related, and machine-related features [11,12]. Previous studies often consider all the potential attributes to construct GP trees.…”

Section: Introductionmentioning

confidence: 99%

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Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Sitahong

Yuan

et al. 2022

Processes

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Genetic Programming (GP) has been widely employed to create dispatching rules intelligently for production scheduling. The success of GP depends on a suitable terminal set of selected features. Specifically, techniques that consider feature selection in GP to enhance rule understandability for dynamic job shop scheduling (DJSS) have been successful. However, existing feature selection algorithms in GP focus more emphasis on obtaining more compact rules with fewer features than on improving effectiveness. This paper is an attempt at combining a novel GP method, GP via dynamic diversity management, with feature selection to design effective and interpretable dispatching rules for DJSS. 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. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules for the three objective functions. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 13.28%, 12.57%, and 15.62% in the mean tardiness (MT), mean flow time (MFT), and mean weighted tardiness (MWT) objective, respectively.

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