管理工程学报
管理工程學報
관리공정학보
Journal of Industrial Engineering and Engineering Management
2015年
1期
106~113
,共null页
企业信息系统 适应性 优化模型 自适应免疫遗传算法
企業信息繫統 適應性 優化模型 自適應免疫遺傳算法
기업신식계통 괄응성 우화모형 자괄응면역유전산법
enterprise information system; adaptability; optimization model; adaptive immune genetic algorithm
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。
為瞭提高企業信息繫統的適應性,研究瞭企業信息繫統的適應性優化問題。首先,建立瞭企業信息繫統適應性的指標體繫,併基于企業信息繫統的形式化錶達方法——對象知識網(Object-based Knowledge Mesh),給齣瞭適應性指標的量化方法;其次,建立瞭企業信息繫統適應性的優化模型,併給齣瞭模型的優化算法——自適應免疫遺傳算法;最後以銷售處理流程為例說明瞭企業信息繫統的適應性指標、優化模型以及算法的應用,驗證瞭其有效性,為提升企業信息繫統的適應性奠定基礎。
위료제고기업신식계통적괄응성,연구료기업신식계통적괄응성우화문제。수선,건립료기업신식계통괄응성적지표체계,병기우기업신식계통적형식화표체방법——대상지식망(Object-based Knowledge Mesh),급출료괄응성지표적양화방법;기차,건립료기업신식계통괄응성적우화모형,병급출료모형적우화산법——자괄응면역유전산법;최후이소수처리류정위례설명료기업신식계통적괄응성지표、우화모형이급산법적응용,험증료기유효성,위제승기업신식계통적괄응성전정기출。
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 for enterprises. Information systems need to be reliable and robust for adaption. Thus,robustness is considered the third indicator. If the adjustment is too complex,implementation will be difficult and the degree of confusion will increase. Thus,complexity indicator also needs to be considered. Finally,any adjustment involves risks are looked at. Therefore,risk is included into the indicator system. In summary,considering internal and external environments of EIS,as well as the related references of EIS properties,the indicator system of EIS adaptability consists of five aspects: time,cost,complexity,robustness and risk. In the meantime,complex and robust indicators are obtained based on the formal representation of EIS quantification. These indicators lay the foundation for EIS adaptability optimization model.In the second part,the EIS adaptability optimization model is established and the optimization algorithm is proposed. According to the EIS adaptability indicator system,the adaptability optimization is a multi-objective optimization problem. The EIS adaptability optimization model is proposed based on the weighted ideal distance. To determine the weight,a multi-objective weight combination algorithm is proposed. An improved immune genetic algorithm,named IGA,is proposed to solve the optimization model. In the algorithm,IGA is improved by adaptive operators. In comparison with traditional GA,IGA's memory function can accelerate searching speed,shorten search time and save costs. Moreover,IGA can overcome familiar premature phenomena to a great extent.In the third part,the application strategies of EIS adaptability optimization are studied. Combining with the indicator system of EIS adaptability and its quantification,as well as the formal representation of EIS,optimized EIS can be obtained with related indicators,such as MLD and IDM. The improved process,corresponding module chart,and information systems structure can be obtained,according to the application strategies.In the fourth part,a case of EIS adaptability optimization is studied. As to the sales process in a certain enterprise,the corresponding value of adaptability indicators and their ideal values can be obtained. Regression analysis is conducted with MiniT ab.Thus,the parameters in the optimization model are obtained. The parameters in the optimization algorithm are fixed,and the optimization model is solved by IGA. According to the application strategies,optimized indicator values and parameter values are obtained. In contrast with the original sales process,the optimized one has better adaptability.The findings of this study show that enterprises can improve their ability of adapting to dynamic environment by optimizing business processes and software module in order to ensure that EIS can be used to deliver specific needs of enterprises. The systematic study of EIS adaptability optimization is helpful to construct information systems with good properties,and realize the information flow in order to enhance competitiveness for companies. This study enriches the theory of EIS adaptability,provides decision making basis for enterprises,and lays theoretical basis for the improvement of EIS adaptability.