Hybrid genetic algorithm based on bin packing strategy for the unrelated parallel workgroup scheduling problem

Su, Bentao; Xie, Naiming; Yang, Yingjie

doi:10.1007/s10845-020-01597-8

Cited by 25 publications

(9 citation statements)

References 26 publications

(14 reference statements)

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“…The full-probability selection operator selects the optimal individual after completing the fitness value calculation and the non-dominated hierarchical sorting, that is, the optimal water output at a certain moment. Then, the optimal individual is put into the next generation to ensure that the population has a feasible solution during evolution, through continuous iteration of the optimal water output at different times [13,14].…”

Section: Improved Genetic Operation Strategymentioning

confidence: 99%

Coupling optimization model of hydropower station power dispatching based on complex dispatching constraints and cascade joint commissioning

Xie¹,

Zhang

2022

J. Phys.: Conf. Ser.

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In order to increase the average power generation of a hydropower station and reduce its water consumption, this paper proposes a hydropower dispatching coupling optimization model based on complex dispatch constraints and cascade-connected dispatching. The power generation water consumption and the remaining load of the power grid are used as the objective function to construct a power dispatch coupling optimization model. After all the constraints are met to solve the objective function, the improved genetic algorithm and the coupling constraint adjustment strategy are combined to form a hybrid search method to realize the genetic operation. The strategy is improved to complete the optimization of power dispatch. The experimental results show that the above model can effectively realize the optimization of power dispatch and increase the average power generation of hydropower stations. It is hoped that the model can reasonably optimize the power dispatch during the flood season and the dry season, thereby effectively reducing the water consumption of the cascade and increasing the power generation of the cascade.

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Section: Improved Genetic Operation Strategymentioning

confidence: 99%

Coupling optimization model of hydropower station power dispatching based on complex dispatching constraints and cascade joint commissioning

Xie¹,

Zhang

2022

J. Phys.: Conf. Ser.

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“…GA is one of the general algorithms for solving complex system problems. Due to its unrestricted and robust universal framework, coupled with its simple principle and easy-to-operate characteristics, GA has become a practical tool for solving problems such as function optimization and combinatorial optimization [28].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Hybrid Knowledge Extraction Framework Using Modified Adaptive Genetic Algorithm and BPNN

Ding

et al. 2022

IEEE Access

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Fault diagnosis based on the expert system (ES) is still a research topic of manufacturing in the Industry 4.0 because of the stronger interpretability. As the core component of the ES, fault diagnosis accuracy is positively correlated to the precise of the knowledge base. But it is difficult for users to understand the knowledge obtained from the original dataset utilizing the existing knowledge extraction method. Therefore, it is of great significance to extract easy-to-understand and exact rules from the NN framework. This paper proposes a hybrid extraction framework to perform the rule extraction for overcoming this drawback. First, an improved adaptive genetic algorithm (GA) using a logistic function, namely LAGA, is proposed to solve the traditional GA's insufficient prediction performance issue. Compared with the other three mainstream adaptive GAs, the experiment results of optimizing six selected test functions by these GA variants show that the LAGA algorithm's convergence accuracy and speed have been greatly improved, especially for high latitude functions. On this basis, a rule extraction method based on symbol rule and NN, namely the LAGA-BP framework, is discussed in this manuscript to classify the real-valued attributes. This framework obtains hidden knowledge (knowledge refinement process) by NN and further transforms the acquired hidden knowledge into more easy-to-understand rule knowledge (rule extraction process). The execution of the LAGA-BP framework could be separated into two phases. The first phase is to optimize a back propagation NN (BPNN) using the LAGA and refine prediction classification knowledge over the optimized BPNN. In the second phase, an attribute reduction algorithm using multi-layered NN (SD algorithm) based on two different superposed networks is used on this framework to reduce data-set attributes and then uses the K-means clustering algorithm to extract the ifthen rule from the simplified attributes. the Wisconsin breast cancer data-set is used as a case study to reveal the correctness and robustness of the proposed LAGA-BP method. Consulting relevant medical personnel and referencing relevant data shows that the rules extracted using this method help verify the diagnosis results, thus verifying the proposed framework's feasibility and practicality.

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“…A hybrid genetic algorithm based on bin packing strategy was proposed by [52] to solve unrelated parallel workgroup scheduling problem where each workgroup is composed of a number of personnel with similar work skills which has eligibility and human resource constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Macro and Micro Scheduling Model for Regulated Safety Inspections Based on Probability Integral Transform and Modified Bin Packing Algorithm

Zytoon

2021

IEEE Access

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Occupational safety and health (OSH) inspection is an essential element of OSH regulatory enforcement programs. The effectiveness of regulated OSH inspections is dependent on many factors of which proper planning and scheduling of inspections, and optimum utilization of inspectors are extremely important. In spite of this, there is no published scheduling model to solve the problem of regulated safety inspections. Absence of such models negatively affected the performance of safety inspectorates in many countries which is manifested by low portion of firms being inspected over long periods of time. For these, there is urgent need to introduce new scheduling models to solve such problem that has distinctive characteristics compared to the scheduling problems in other fields. The current paper presents a novel hybrid macro and micro scheduling approach that is based on Probability Integral Transform (PIT) and a modified Bin Packing algorithm that address the Inspector Utilization Problem with Firm Inspection Sharing (IUPFISh). The PIT-IUPFISh model consists of uniform long-term macro scheduling of firms for inspection using PIT approach coupled with short-term micro scheduling of firm inspection using the IUPFISh algorithm to determine the daily scheduling of the firms and inspectors so that maximum utilization of inspectors is achieved. A case study was presented to show the application of the proposed model. The results indicate that the application of the proposed model can improve OSH inspectorate performance through improved planning and scheduling as well as maximizing effective utilization of manpower.

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Hybrid genetic algorithm based on bin packing strategy for the unrelated parallel workgroup scheduling problem

Cited by 25 publications

References 26 publications

Coupling optimization model of hydropower station power dispatching based on complex dispatching constraints and cascade joint commissioning

Coupling optimization model of hydropower station power dispatching based on complex dispatching constraints and cascade joint commissioning

Hybrid Knowledge Extraction Framework Using Modified Adaptive Genetic Algorithm and BPNN

A Novel Macro and Micro Scheduling Model for Regulated Safety Inspections Based on Probability Integral Transform and Modified Bin Packing Algorithm

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