Multi-objective optimal scheduling of automated construction equipment using non-dominated sorting genetic algorithm (NSGA-III)

Liu, Ying; You, Ke; Jiang, Yutian; Wu, Zhangang; Liu, Zhenyuan; Peng, Gang; Zhou, Cheng

doi:10.1016/j.autcon.2022.104587

Cited by 22 publications

(7 citation statements)

References 67 publications

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“…Two types of experiments are carried out: the convergence and evenness of the non-dominated solutions generated by GDOA are analyzed, and the social welfare of the schedule generated by GDOA is verified. During the experiments, GDOA is compared against a well-known centralized algorithm NSGA-III [34] and two decentralized algorithms, namely, a generic negotiation mechanism (GNMS) [20] and a genetic decision-based two-stage negotiation algorithm (GTNA) [13]. All algorithms are implemented in Python 3.9, and the experiments are carried out on a PC with an Intel Core i7-7700 3.60 GHz CPU and 16 GB of RAM.…”

Section: Computational Experimentsmentioning

confidence: 99%

A Decentralized Optimization Algorithm for Multi-Agent Job Shop Scheduling with Private Information

Zhou,

Rao,

Liu

et al. 2024

Mathematics

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The optimization of job shop scheduling is pivotal for improving overall production efficiency within a workshop. In demand-driven personalized production modes, achieving a balance between workshop resources and the diverse demands of customers presents a challenge in scheduling. Additionally, considering the self-interested behaviors of agents, this study focuses on tackling the problem of multi-agent job shop scheduling with private information. Multiple consumer agents and one job shop agent are considered, all of which are self-interested and have private information. To address this problem, a two-stage decentralized algorithm rooted in the genetic algorithm is developed to achieve a consensus schedule. The algorithm allows agents to evolve independently and concurrently, aiming to satisfy individual requirements. To prevent becoming trapped in a local optimum, the search space is broadened through crossover between agents and agent-based block insertion. Non-dominated sorting and grey relational analysis are applied to generate the final solution with high social welfare. The proposed algorithm is compared using a centralized approach and two state-of-the-art decentralized approaches in computational experiments involving 734 problem instances. The results validate that the proposed algorithm generates non-dominated solutions with strong convergence and uniformity. Moreover, the final solution produced by the developed algorithm outperforms those of the decentralized approaches. These advantages are more pronounced in larger-scale problem instances with more agents.

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Section: Computational Experimentsmentioning

confidence: 99%

A Decentralized Optimization Algorithm for Multi-Agent Job Shop Scheduling with Private Information

Zhou,

Rao,

Liu

et al. 2024

Mathematics

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“…The efficiency optimization scheduling of construction machinery needs to be based on task type, workload, and the working capacity of different equipment [7]. For general highway construction, it is usually necessary to cooperate with excavators, loaders, bulldozers, and rollers to complete.…”

Section: Optimization Model Establishmentmentioning

confidence: 99%

Multi-objective construction machinery group configuration optimization based on improved particle swarm optimization algorithm

Gao,

Yang,

Zhou

2024

Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023)

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“…Te objective optimization method has been efectively used in various areas of research; it originated from multiobjective optimization algorithms with exceptional performance, such as NSGA [35][36][37], PSO [38,39], and Jaya [40,41]. Among many multiobjective optimization algorithms, NSGA can ensure the uniform distribution of the nondominated optimal solution, the diversity of the population, and high computational efciency [42][43][44].…”

Section: Multiobjective Optimization Algorithmmentioning

confidence: 99%

The Use of Shape Accel Array for Deformation Monitoring and Parameter Inversion of a 300 m Ultrahigh Rockfill Dam

Zhang

et al. 2023

Structural Control and Health Monitoring

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The global construction of rockfill dams has now surpassed the 300 m height level. Despite great achievements in dam design and construction, monitoring techniques have lagged behind the development of high rockfill dams. Existing deformation monitoring techniques are ill-suited to the high earth and water pressures, and extended monitoring periods are required for ultrahigh rockfill dams. This study introduces, for the first time, the use of a shape accel array (SAA) to monitor internal displacement in a 300 m high earth core rockfill dam. The SAA employs a rope-like array of capacitive MEMS accelerometers for deformation measurement. Compared to conventional monitoring techniques, SAA is a data-intensive monitoring technique. Based on the intensive data obtained from SAA, we employed a parameter inversion method, utilizing multiobjective optimization algorithm, the nondominated sorting genetic algorithm-III (NSGA-III), to inverse the constitutive model parameters of the rockfill dam. The multiobjective parameter inversion method maximizes the use of multisource monitoring data for predicting rockfill dam deformation.

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Multi-objective optimal scheduling of automated construction equipment using non-dominated sorting genetic algorithm (NSGA-III)

Cited by 22 publications

References 67 publications

A Decentralized Optimization Algorithm for Multi-Agent Job Shop Scheduling with Private Information

A Decentralized Optimization Algorithm for Multi-Agent Job Shop Scheduling with Private Information

Multi-objective construction machinery group configuration optimization based on improved particle swarm optimization algorithm

The Use of Shape Accel Array for Deformation Monitoring and Parameter Inversion of a 300 m Ultrahigh Rockfill Dam

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