{"title":"基于Coxian分布的工程项目过程模型分析","authors":"Y. Tao","doi":"10.1109/PHM2022-London52454.2022.00027","DOIUrl":null,"url":null,"abstract":"With the development of social economy, the scale and quantity of engineering projects are growing rapidly. The difficulty of engineering project management is also growing. Traditional management methods have become increasingly unable to meet the needs of modern engineering project management. New theoretical methods are needed to optimize the engineering project process. This paper considers the overall engineering project as a complex system. This complex system is composed of a series of sub-engineering projects and refines the engineering project management process. The time of sub-engineering project is calculated on the assumption that the independent engineering project time distribution obeys the exponential distribution. Then the engineering project management process uses the Coxian distribution to build a calculation model. Firstly, the engineering project process is divided into several phases. Then the k-order Erlang distribution is divided for each stage, and the phase time distribution is solved. Based on the given sub-engineering projects, the Erlang distributions of each stage are mixed to obtain the Coxian distribution model. Finally, the feasibility of the model is verified by data analysis. By combining the Coxian distribution, this paper considers multiple engineering projects as a whole and studies the impact of the sub-engineering projects on the whole engineering project. The completion degree of the entire multi-engineering project process performs mathematical modeling. There are theoretical and practical significance in actual engineering projects. In addition, the results show that the model is more consistent with the reality.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Model analysis of engineering project process based on Coxian distribution\",\"authors\":\"Y. Tao\",\"doi\":\"10.1109/PHM2022-London52454.2022.00027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of social economy, the scale and quantity of engineering projects are growing rapidly. The difficulty of engineering project management is also growing. Traditional management methods have become increasingly unable to meet the needs of modern engineering project management. New theoretical methods are needed to optimize the engineering project process. This paper considers the overall engineering project as a complex system. This complex system is composed of a series of sub-engineering projects and refines the engineering project management process. The time of sub-engineering project is calculated on the assumption that the independent engineering project time distribution obeys the exponential distribution. Then the engineering project management process uses the Coxian distribution to build a calculation model. Firstly, the engineering project process is divided into several phases. Then the k-order Erlang distribution is divided for each stage, and the phase time distribution is solved. Based on the given sub-engineering projects, the Erlang distributions of each stage are mixed to obtain the Coxian distribution model. Finally, the feasibility of the model is verified by data analysis. By combining the Coxian distribution, this paper considers multiple engineering projects as a whole and studies the impact of the sub-engineering projects on the whole engineering project. The completion degree of the entire multi-engineering project process performs mathematical modeling. There are theoretical and practical significance in actual engineering projects. In addition, the results show that the model is more consistent with the reality.\",\"PeriodicalId\":269605,\"journal\":{\"name\":\"2022 Prognostics and Health Management Conference (PHM-2022 London)\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Prognostics and Health Management Conference (PHM-2022 London)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PHM2022-London52454.2022.00027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Prognostics and Health Management Conference (PHM-2022 London)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PHM2022-London52454.2022.00027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Model analysis of engineering project process based on Coxian distribution
With the development of social economy, the scale and quantity of engineering projects are growing rapidly. The difficulty of engineering project management is also growing. Traditional management methods have become increasingly unable to meet the needs of modern engineering project management. New theoretical methods are needed to optimize the engineering project process. This paper considers the overall engineering project as a complex system. This complex system is composed of a series of sub-engineering projects and refines the engineering project management process. The time of sub-engineering project is calculated on the assumption that the independent engineering project time distribution obeys the exponential distribution. Then the engineering project management process uses the Coxian distribution to build a calculation model. Firstly, the engineering project process is divided into several phases. Then the k-order Erlang distribution is divided for each stage, and the phase time distribution is solved. Based on the given sub-engineering projects, the Erlang distributions of each stage are mixed to obtain the Coxian distribution model. Finally, the feasibility of the model is verified by data analysis. By combining the Coxian distribution, this paper considers multiple engineering projects as a whole and studies the impact of the sub-engineering projects on the whole engineering project. The completion degree of the entire multi-engineering project process performs mathematical modeling. There are theoretical and practical significance in actual engineering projects. In addition, the results show that the model is more consistent with the reality.