Small-World Optimization Algorithm and Its Application in a Sequencing Problem of Painted Body Storage in a Car Company

Tian, Zhen; Shao, Xinyu; Zhu, Haiping; Yin, Hui; He, Fazhi

doi:10.1155/2015/932502

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Tabar et al proposed a recognition and sequence optimization method for geometric spot welding in a digital twin environment, which optimized the production sequence of the welding workshop [10]. Tian et al proposed a small-world optimization algorithm and applied it to the resequencing problem between the painting workshop and the assembly workshop [11]. Yilmazlar and Kurz proposed an adaptive local search algorithm for car sequencing problem, and they demonstrated that the algorithm should focus on randomly exploring more non-degenerate solutions rather than spending time accessing degenerate solutions [12].…”

Section: Related Workmentioning

confidence: 99%

“…The larger the value of HV, the better the solution obtained by the algorithm, and vice versa. The calculation method of HV is shown in Formula (11). In addition, to verify the significance of the differences between algorithms, a Wilcoxon rank-sum test was used to test the significance of the differences between the two algorithms, with a significance level set at 0.05.…”

Section: Parameter Setting and Performance Evaluation Indicatorsmentioning

confidence: 99%

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A Hybrid Evolutionary Algorithm for Multi-Stage Workshop Sequencing in Car Production

Chen,

Liu,

Liu

2023

Processes

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During the car production process, diverse production workshops have distinct prerequisites for car body sequencing. This results in the intricate nature of sequencing within multi-stage car workshops. In this study, an optimization method for car body sequencing is proposed that combines a hybrid evolutionary algorithm with heuristic rules. In the welding workshop, a genetic algorithm is employed to optimize the vehicle sequencing. Simultaneously, a differential evolution algorithm is used to optimize the inbound sequence of the buffer zone between the welding and painting workshops, as well as the inbound sequence of the buffer zone between the painting and assembly workshops. Heuristic rules are applied to optimize the outbound sequence of the buffer zone between the welding and painting workshops, as well as the outbound sequence of the buffer zone between the painting and assembly workshops. In addition, in order to improve the quality of the initial population, a heuristic method-based initial population construction method is proposed. The optimization objectives are the number of vehicle model changes in the welding workshop, the number of color changes in the painting workshop, and the total number of overloads in the assembly workshop. The experimental results show that the proposed method performs better than the five outstanding evolutionary algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Parameter Setting and Performance Evaluation Indicatorsmentioning

confidence: 99%

A Hybrid Evolutionary Algorithm for Multi-Stage Workshop Sequencing in Car Production

Chen,

Liu,

Liu

2023

Processes

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“…SWOA believes that the search process is a basic of information transmission process in search space networks that both operators provided. SWOA was applied in few areas to address optimizations that include network routing problem [244], automobile sequencing problem [245], neural network problem [246]. At the present time of this writing, SWOA is not adopted to any t-way testing strategy.…”

Section: Physic-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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“…Moon et al [15] proposed a storage/retrieval algorithm to simulate the dynamic operation of automobile PBS and verify the proposed system. Tian et al [16] analyzed the scheduling of the PBS sequencing process, which contains upstream sequence inbound and downstream sequence outbound, through a two-stage algorithm. In the first stage, the discrete small-world optimization algorithm (DSWOA) was used to schedule the inbound sequence, and in the second stage, a heuristic algorithm was used to schedule the outbound sequence.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Optimization for Car PBS Scheduling Based on Greedy Strategy State Transition Algorithm

Peng

et al. 2023

Applied Sciences

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The differences in constraints between an automotive painting workshop and a final assembly workshop can lead to production scheduling disruptions, and using the Painted Body Store (PBS) in automotive manufacturing can optimize production scheduling to meet the production requirements of the final assembly workshop. This is achieved by establishing a multi-objective mixed-integer optimization scheduling model for PBS and solving the model using a state transition algorithm based on the greedy strategy; finally, a comparison was made with the classical genetic algorithm. The results show that the optimal objective function is achieved when the body sequence movement iterations for data sets N and R are 34 and 54, respectively. At this point, the highest objective function scores are 23.46 and 49.79, and vehicles entering the PBS scheduling system from the painting-to-assembly line require 31 and 38 sequence adjustments, respectively. Compared with the classical genetic algorithm, the simulation results show that the state transition algorithm based on the greedy strategy is significantly better; it can also perform well in discrete sequence movement operations, demonstrating strong global search capabilities and fast convergence. It provides a new approach for optimizing vehicle scheduling in the automotive painting-to-assembly line.

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Small-World Optimization Algorithm and Its Application in a Sequencing Problem of Painted Body Storage in a Car Company

Cited by 6 publications

References 22 publications

A Hybrid Evolutionary Algorithm for Multi-Stage Workshop Sequencing in Car Production

A Hybrid Evolutionary Algorithm for Multi-Stage Workshop Sequencing in Car Production

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

An Analysis of Optimization for Car PBS Scheduling Based on Greedy Strategy State Transition Algorithm

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