Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling

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

doi:10.1109/tevc.2023.3255246

Cited by 29 publications

(11 citation statements)

References 249 publications

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“…As a well-known optimization approach, GPHH has been widely used in all kinds of optimization problems, especially the combinatorial optimization problem represented by the JSSP [7,35]. As a hyper-heuristic method, GPHH does not directly find a specific solution to the problem but iteratively generates a set of heuristic rules to guide the solution generation.…”

Section: Solving the Dfjssp Based On Gphhmentioning

confidence: 99%

“…JSSP entails the efficient allocation of production resources for a decomposable processing task under certain constraints to optimize performance metrics, such as total processing time, flow time, and tardiness [4]. The flexible job shop scheduling problem (FJSSP) allows for more flexible use of machine resources compared to the JSSP, where each operation of a job can be processed on multiple candidate machines, better reflecting real-world situations [5][6][7]. Therefore, it is essential to first assign jobs to suitable machines (i.e., the routing decisions) in the FJSSP and then sequence the jobs in the waiting queue of each machine for processing (i.e., the sequencing decisions).…”

Section: Introductionmentioning

confidence: 99%

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Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Xu,

Fei

et al. 2024

Mathematics

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Considering the requirements of the actual production scheduling process, the utilization of the genetic programming hyper-heuristic (GPHH) approach to automatically design dispatching rules (DRs) has recently emerged as a popular optimization approach. However, the decision objects and decision environments for routing and sequencing decisions are different in the dynamic flexible job shop scheduling problem (DFJSSP), leading to different required feature information. Traditional algorithms that allow these two types of scheduling decisions to share one common feature set are not conducive to the further optimization of the evolved DRs, but instead introduce redundant and unnecessary search attempts for algorithm optimization. To address this, some related studies have focused on customizing the feature sets for both routing and sequencing decisions through feature selection when solving single-objective problems. While being effective in reducing the search space, the selected feature sets also diminish the diversity of the obtained DRs, ultimately impacting the optimization performance. Consequently, this paper proposes an improved GPHH with dual feature weight sets for the multi-objective energy-efficient DFJSSP, which includes two novel feature weight measures and one novel hybrid population adjustment strategy. Instead of selecting suitable features, the proposed algorithm assigns appropriate weights to the features based on their multi-objective contribution, which could provide directional guidance to the GPHH while ensuring the search space. Experimental results demonstrate that, compared to existing studies, the proposed algorithm can significantly enhance the optimization performance and interpretability of energy-efficient DRs.

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Section: Solving the Dfjssp Based On Gphhmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Xu,

Fei

et al. 2024

Mathematics

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“…To address the above shortcomings, we employ Genetic Programming (GP) [53,54], one of the most prominent methods in (evolutionary) machine learning, to automatically design the state transition rules in ACO. Genetic Programming has made significant advancements in the field of automatic algorithm design, successfully tackling various complex combinatorial optimization problems [55][56][57]. GP algorithms do not rely on prior knowledge of the problem and offer excellent interpretability [52].…”

Section: Introductionmentioning

confidence: 99%

Automated Design of State Transition Rules in Ant Colony Optimization by Genetic Programming: A Comprehensive Investigation

Lin,

Mei,

Zhang

2024

Preprint

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The automated design of Ant Colony Optimization (ACO) algorithms has become increasingly significant, particularly in addressing complex combinatorial optimization problems. Although existing methods have achieved some success, they still face limitations, particularly the high dependency on expert knowledge, pre-solved data, and challenges in interpretability. Genetic Programming (GP), as a proven technology, has shown potential in optimizing the automated design state transition rules of ACO. However, existing research on GP-ACO is insufficient, particularly in terms of experimental validation and systematic evaluation. To address these issues, this study conducts comprehensive experiments to explore several key questions: the generality of GP-ACO on homogeneously distributed maps, the impact of different ACO variants on the learning capabilities of GP-ACO, the effect of 2-opt local search on the learning capabilities of GP-ACO, the enhancement of learning capabilities through the addition of more global information, and the interpretability of GP-ACO. The findings indicate that GP-ACO exhibits robust generality; variations among ACO variants have minimal impact on learning performance; 2-opt local search can somewhat diminish the performance of GP-ACO in the Max-Min Ant System (MMAS); additional global information can significantly enhance the learning capabilities of GP-ACO; and GP-ACO has good interpretability.

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“…There are also many open-source libraries and toolkits available for evolutionary computation in a variety of programming languages [32][33][34][35][36][37][38][39][40][41], making the application of evolutionary algorithms to new problems and domains particularly easy. Evolutionary computation has been effective in solving problems with a variety of characteristics, and within many application domains, such as multiobjective optimization [42][43][44][45], data science [46], machine learning [47][48][49], classification [50], feature selection [51], neural architecture search [52], neuroevolution [53], bioinformatics [54], scheduling [55], algorithm selection [56], computer vision [57], hardware validation [58], software engineering [59,60], and multi-task optimization [61,62], among many others.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Computation: Theories, Techniques, and Applications

Cicirello

2024

Applied Sciences

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Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling

Cited by 29 publications

References 249 publications

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Automated Design of State Transition Rules in Ant Colony Optimization by Genetic Programming: A Comprehensive Investigation

Evolutionary Computation: Theories, Techniques, and Applications

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