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首页> 外文期刊>International journal of business information systems >A hybrid GA-ant colony approach for exploring the relationship between IT and firm performance
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A hybrid GA-ant colony approach for exploring the relationship between IT and firm performance

机译:探究IT与企业绩效之间关系的混合GA-蚁群方法

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摘要

Several studies were conducted during recent years on exploring the impact of Information Technology (IT) on the performance of the organisation. It is quite important to find a robust technique to identify the relationship between IT and organisational performance. A hybrid Genetic Algorithm (GA) Ant Colony Optimisation (ACO) approach is proposed for data clustering. This is because of the need for the application of metaheurisitic algorithms parallel to deterministic approaches. This study discusses and analyses data from 90 companies in a unique supply chain. The data includes 26 indices about IT and 11 indices about performance. The companies are classified with respect to the IT and performance indices (indicators). Then, IT clusters and performance clusters are mapped to one another and, consequently, the relationship between them is explored. In general, the result shows that there is a linear relationship between the IT status and performance of the companies, with few exceptions. This is the first study which integrates ant colony approach and GA for exploring the relationship between IT and firm performance.
机译:近年来,进行了几项研究,以探讨信息技术(IT)对组织绩效的影响。找到一种可靠的技术来识别IT与组织绩效之间的关系非常重要。提出了一种混合遗传算法(GA)蚁群优化(ACO)方法进行数据聚类。这是因为需要与确定性方法并行应用元启发式算法。这项研究讨论和分析了独特供应链中90家公司的数据。该数据包括有关IT的26个索引和有关性能的11个索引。根据IT和绩效指数(指标)对公司进行分类。然后,将IT集群和性能集群相互映射,从而探索它们之间的关系。通常,结果表明,除了少数例外,IT状态与公司绩效之间存在线性关系。这是第一项将蚁群方法与遗传算法相结合的研究,旨在探索IT与企业绩效之间的关系。

著录项

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  • 作者单位

    Department of Industrial Engineering Center of Excellence for Intelligent Experimental Mechanics Department of Engineering Optimization Research College of Engineering University of Tehran, Iran;

    Department of Industrial Engineering Center of Excellence for Intelligent Experimental Mechanics Department of Engineering Optimization Research College of Engineering University of Tehran, Iran;

    Department of Industrial Engineering Center of Excellence for Intelligent Experimental Mechanics Department of Engineering Optimization Research College of Engineering University of Tehran, Iran;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    information technology; performance; cluster analysis; ant colony; optimisation; genetic algorithms; hybrid;

    机译:信息技术;性能;聚类分析;蚁群优化;遗传算法;杂种;

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