Manufacturing resource combinatorial optimization for large complex equipment in group manufacturing: A cluster-based genetic algorithm

Guo, Sen; Du, Baigang; Zhao, Peng; Huang, Xiaorong; Li, Yibing

doi:10.1016/j.mechatronics.2015.03.005

Cited by 16 publications

(5 citation statements)

References 36 publications

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“…Nowadays, with the passage of time, various algorithms have been proposed, such as, Gradient Descent, Stochastic Gradient Descent, Mini-Batch Gradient Descent, Newton's Method and so on. For example, in researcher Weidong Guo's optimization and simulation research on the objective function of vehicle vibration model, Guo claims that when optimizing the objective function, only optimizing a single objective function is too one-sided to make the car comfortable for passengers and drivers during driving [4]. Therefore, researcher Guo accords the practical situations to adopt Genetic Algorithm to optimize the objective function.…”

Section: Basic Descriptionmentioning

confidence: 99%

Analysis of the Principle and Application of Optimization

2024

HSET

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As a matter of fact, optimization approaches are widely used in lots of fields to find the best solutions for certain issues in recent years. In reality, the optimization refers to the use of optimization algorithms or models to find the overall or local optimal goal of the optimization problem under certain constraints. The optimization principle has been widely applied in various practical fields, such as power system engineering design, internet fields, production management, logistics planning, financial investment. With this in mind, this article will introduce and analyze the principle of optimization and applications of optimization principles in some fields. To be specific, the equations, solutions as well as the effectiveness of the applications will be analyzed and demonstrated. According to the analysis, the current limitations as well as the prospects will be given at the same time. Overall, these results shed light on guiding further exploration of implementation for optimization.

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Section: Basic Descriptionmentioning

confidence: 99%

Analysis of the Principle and Application of Optimization

2024

HSET

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“…The only requirement for fitness function is the positive output that can be calculated for input. The characteristics of GA make the application scope extend greatly (Guo et al, 2015).…”

Section: Evaluation Of Individuals Only By the Fitness Function Valuementioning

confidence: 99%

Research on the optimisation of complex models of large-scale building structures dependent on adaptive grey genetic algorithms

Shi

2020

IJBM

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Genetic algorithm (GA) is a bionics algorithm based on the biological evolution theory that has received extensive attention in the field of computer science and optimisation in recent years. This paper analyses and integrates the relevant contents of genetic algorithm and its application in the optimal design of large-scale building structures and analyses and researches briefly several key factors when the genetic algorithm is applied to the optimal design of large-scale building structures, such as mathematical modelling, constraint condition treatment, generation of initial population and selection of control parameters of genetic algorithm. However, because the simple genetic algorithm is only good at global search, and the local search ability is not enough, it will take quite a long time to achieve the real optimal solution. For the shortcomings of simple genetic algorithm, an improved adaptive grey genetic algorithm is proposed in this paper. The example shows that the obtained adaptive genetic algorithm can improve the convergence and calculation speed when the genetic algorithms is applied to structural optimisation design.

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“…Zheng et al proposed a fuzzy theory-based service quality measurement model to achieve optimal resource services [14]. Guo et al used fuzzy averaging algorithm to determine the priority of multiple objectives for the optimization problem of manufacturing resource combination in group production [15]. Lee et al proposed a smart data management induced resource allocation system consisting of product material, people, information, control, and support functions to ensure productivity [8].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin-driven Multi-resource Constrained Location System for Resource Allocation

Qi¹,

2023

Preprint

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Smart manufacturing systems combine sensor systems and manufacturing processes, and they have been widely adopted in the industry to solve real production problems, help manufacturing enterprises achieve rapid decision making, and improve manufacturing value. However, manufacturing enterprises still face huge challenges with the coexistence of continuously changing dynamic demands, collaborative scheduling of dynamic resources, precise matching of manufacturing resources, and multiple resource constraints. To address this challenge, this research combines digital twin (DT) technology to propose a smart site-selection system with dynamic resource-accurate matching characteristics based on the attributes and associations of both resource sides, supply and demand sides, and site-selection sides, which can integrate and optimize resources according to the requirements of manufacturing tasks. In addition, by establishing the discovery mechanism of bottleneck processes and resource allocation methods, generating configuration priorities and thus reducing the solution space for resource allocation, the precise allocation of limited resources is achieved more quickly and easily, and the scheduling chaos in the parallel scheduling of multiple resources is solved and the multi-objective robust optimization model is solved by combining smart optimization algorithms. Combined with the example analysis, the results show that the smart site-selection system and multi-resource cyclic allocation mechanism proposed in this paper can collaboratively match a large amount of dynamic resources, and the utilization rate of idle manufacturing resources can be increased by 60%. This research effectively realizes the optimal allocation of multiple manufacturing resources in a resource-constrained environment and helps manufacturing enterprises create more manufacturing value.

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Manufacturing resource combinatorial optimization for large complex equipment in group manufacturing: A cluster-based genetic algorithm

Cited by 16 publications

References 36 publications

Analysis of the Principle and Application of Optimization

Analysis of the Principle and Application of Optimization

Research on the optimisation of complex models of large-scale building structures dependent on adaptive grey genetic algorithms

A Digital Twin-driven Multi-resource Constrained Location System for Resource Allocation

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