基于自适应免疫遗传算法的企业信息系统适应性优化研究  

Enterprise Information System Adaptability Optimization Based on Adaptive Immune Genetic Algorithm

作　　者：薛朝改[1] 

机构地区：[1]郑州大学管理工程系,河南郑州450001

出　　处：《管理工程学报》2015年第1期106-113,共8页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(70971119;70901066)

摘　　要：为了提高企业信息系统的适应性,研究了企业信息系统的适应性优化问题。首先,建立了企业信息系统适应性的指标体系,并基于企业信息系统的形式化表达方法——对象知识网(Object-based Knowledge Mesh),给出了适应性指标的量化方法;其次,建立了企业信息系统适应性的优化模型,并给出了模型的优化算法——自适应免疫遗传算法;最后以销售处理流程为例说明了企业信息系统的适应性指标、优化模型以及算法的应用,验证了其有效性,为提升企业信息系统的适应性奠定基础。It is necessary for enterprises to improve the adaptability of enterprise information system（ EIS） or EIS adaptability because they need to continuously cope with changing internal and external environments,such as information explosion,market,law,and government regulations. The improvement of adaptability can lead to the improvement of efficiency,the reduction of cost,and the increase of competition capacity and profit. Improving the degree of EIS adaptability is challenging in the development and application of EIS. Therefore,it is necessary to study EIS adaptability in order to further develop and apply EIS.In order to improve EIS adaptability,this paper studies the optimization of EIS adaptability. Firstly,we propose an indicator system to measure EIS adaptability. This system includes five indicators： time,cost,complexity,robust,and risk. In addition,each indicator contains sub-indicators. The indicators of EIS adaptability are quantified based on the formal representation of EIS,objectbased knowledge mesh（ OKM）. Secondly,based on the EIS adaptability indicator system and its quantification,the multi-objective optimization model of EIS adaptability is established and the corresponding improved optimization algorithm; that is the adaptive immune genetic algorithm（ IGA）,is given. Thirdly,application strategies are proposed by combining the indicator system of EIS adaptability and its quantification with formal representation of EIS. Finally,a sales process is adopted as an example to show the application of the indicator system,optimization model and improved optimization algorithm. Optimization results show that the optimized system has better adaptability,feasibility,and effectiveness of the proposed methods and application strategies.In the first part,the indicator systems of EIS adaptability and its quantification are firstly studied. To respond to changing and unpredictable market,time is the first indicator to be considered in EIS adaptability. In addition,the adjustment must be feasible fo

关 键 词：企业信息系统 适应性 优化模型 自适应免疫遗传算法 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]