面向区块链的协同业务建模及其并行优化算法

上官子卓; 李东鹤; 杨清宇

doi:10.7652/xjtuxb202412013

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面向区块链的协同业务建模及其并行优化算法

—————————————————————— | 更新时间：2025-07-28

- 面向区块链的协同业务建模及其并行优化算法
- Collaborative Business Modeling and Parallel Optimization Algorithm for Blockchain
- 西安交通大学学报 2024年58卷第12期页码：131-140
- 作者机构：
  
  1. 西安交通大学自动化科学与工程学院,西安,710049
  2. 西安交通大学精密微纳制造技术全国重点实验室,西安,710049
- 作者简介：
  
  : 2024-07-08。作者简介: 上官子卓(2000—),女,博士生
  杨清宇(通信作者),男,教授,博士生导师。基金项目: 国家重点研发计划资助项目(2021YFB3301700)。
- 基金信息：
  
  : 2024-07-08。作者简介: 上官子卓(2000—),女,博士生;杨清宇(通信作者),男,教授,博士生导师。基金项目: 国家重点研发计划资助项目(2021YFB3301700)。
- DOI：10.7652/xjtuxb202412013
  中图分类号： TP3
- 网络首发：2024-12-10，
  
  纸质出版：2024
- 稿件说明：
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上官子卓, 李东鹤, 杨清宇. 面向区块链的协同业务建模及其并行优化算法[J]. 西安交通大学学报, 2024,58(12):131-140.

SHANGGUAN Zizhuo, LI Donghe, YANG Qingyu. Collaborative Business Modeling and Parallel Optimization Algorithm for Blockchain[J]. 2024, 58(12): 131-140.
上官子卓, 李东鹤, 杨清宇. 面向区块链的协同业务建模及其并行优化算法[J]. 西安交通大学学报, 2024,58(12):131-140. DOI： 10.7652/xjtuxb202412013.

SHANGGUAN Zizhuo, LI Donghe, YANG Qingyu. Collaborative Business Modeling and Parallel Optimization Algorithm for Blockchain[J]. 2024, 58(12): 131-140. DOI： 10.7652/xjtuxb202412013.

摘要

针对供应链管理面临的协同高效性、信息可追溯性和信任危机等问题

提出了一个解决区块链多要素矛盾问题的并行计算模型PNSGA-Ⅲ。首先

深入挖掘区块链、链主企业和链上企业的制约关系

将链主企业成本、服务积分

链上企业参与数量以及区块链稳定性等参数具象为多级多业务协同模型; 其次

从提高遗传算法产生子代缓存区的效率角度出发

将交叉变异部分并行化处理

极大缩短模型求解时间; 此外

为了解决帕累托解不能相互支配的问题

采用TOPSIS计算欧氏距离并决策理想解; 最后

通过10组仿真实验验证了所提模型的有效性。结果表明:PNSGA-Ⅲ的计算时间比非并行算法节约了16.37%; 区块链的矛盾制约机制提供了更广泛的选择范围

增强了其适应复杂需求的能力; 在动态任务流程下

经过优化的S8场景的服务积分比未优化的场景高出45%。研究为供应链管理提供了新的视角和工具。

Abstract

In response to challenges encountered in supply chain management such as collaborative efficiency

information traceability

and trust crises

a parallel computing model

PNSGA-Ⅲ

is introduced to tackle the multifaceted issues in blockchain. Initially

by exploring the interactions among blockchain

the blockchain lead enterprises(BLEs)

and blockchain on-chain enterprises(BOEs)

parameters such as costs for BLEs

service points

the number of participating BOEs

and blockchain stability are transformed into a multi-level multi-business collaborative model. Subsequently

to enhance the efficiency of the genetic algorithm in generating offspring cache areas

the crossover and mutation components are processed in parallel

leading to a substantial reduction in the model's solution time. Furthermore

to handle the challenge of non-mutually dominant Pareto solutions

TOPSIS is employed to calculate the Euclidean distance and determine the optimal solutions. Finally

the effectiveness of the proposed model is verified through 10 sets of simulation experiments. The results indicate that the computation time of PNSGA-Ⅲ is 16.37% shorter than that of non-parallel algorithms. The conflicting constraint mechanism of blockchain provides a wider range of options

enhancing its adaptability to complex requirements. Under dynamic task flows

the service points of the optimized S8 solution are 45% higher than those of the unoptimized solution. This research provides fresh insights and tools for advancing supply chain management practices.

关键词

Keywords

references

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