{"title":"成本不确定性下工程车辆经济寿命等效年成本模型的实例研究","authors":"Mohamad Zarean, A. Sayadi, Amin Mousavi","doi":"10.1080/0013791X.2022.2028048","DOIUrl":null,"url":null,"abstract":"Abstract Whereas the practical importance of the Economic Lifetime (EL) is well-known, selecting the proper process has always been a dilemma. In this respect, classical methods dating back to one century ago are generally favored, but using them in a data-driven approach still has particular shortcomings. This paper aims to present a Life Cycle Cost (LCC) model determining the EL of a truck while fluctuation in historical data deepens through its lifespan. The equivalent annual cost of LCC is developed based on Operating and Maintenance (O&M) costs along with the resale value. The O&M cost was estimated deterministically and stochastically using regression analysis and Brownian-Motion-based simulation. The resale value was modeled by employing a genetic algorithm. The model capability was evaluated using real data of a seven cubic-meters truck hauling rock-fill materials in a dam construction project. The optimal EL was estimated on average 105 months in deterministic condition, while it was 88-145 months at the 70% confidence level using non-deterministic approach.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"67 1","pages":"75 - 93"},"PeriodicalIF":1.0000,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Case study of an equivalent annual cost model for economic lifetime for construction vehicles under cost uncertainty\",\"authors\":\"Mohamad Zarean, A. Sayadi, Amin Mousavi\",\"doi\":\"10.1080/0013791X.2022.2028048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Whereas the practical importance of the Economic Lifetime (EL) is well-known, selecting the proper process has always been a dilemma. In this respect, classical methods dating back to one century ago are generally favored, but using them in a data-driven approach still has particular shortcomings. This paper aims to present a Life Cycle Cost (LCC) model determining the EL of a truck while fluctuation in historical data deepens through its lifespan. The equivalent annual cost of LCC is developed based on Operating and Maintenance (O&M) costs along with the resale value. The O&M cost was estimated deterministically and stochastically using regression analysis and Brownian-Motion-based simulation. The resale value was modeled by employing a genetic algorithm. The model capability was evaluated using real data of a seven cubic-meters truck hauling rock-fill materials in a dam construction project. The optimal EL was estimated on average 105 months in deterministic condition, while it was 88-145 months at the 70% confidence level using non-deterministic approach.\",\"PeriodicalId\":49210,\"journal\":{\"name\":\"Engineering Economist\",\"volume\":\"67 1\",\"pages\":\"75 - 93\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2022-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Economist\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1080/0013791X.2022.2028048\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Economist","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1080/0013791X.2022.2028048","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS","Score":null,"Total":0}
Case study of an equivalent annual cost model for economic lifetime for construction vehicles under cost uncertainty
Abstract Whereas the practical importance of the Economic Lifetime (EL) is well-known, selecting the proper process has always been a dilemma. In this respect, classical methods dating back to one century ago are generally favored, but using them in a data-driven approach still has particular shortcomings. This paper aims to present a Life Cycle Cost (LCC) model determining the EL of a truck while fluctuation in historical data deepens through its lifespan. The equivalent annual cost of LCC is developed based on Operating and Maintenance (O&M) costs along with the resale value. The O&M cost was estimated deterministically and stochastically using regression analysis and Brownian-Motion-based simulation. The resale value was modeled by employing a genetic algorithm. The model capability was evaluated using real data of a seven cubic-meters truck hauling rock-fill materials in a dam construction project. The optimal EL was estimated on average 105 months in deterministic condition, while it was 88-145 months at the 70% confidence level using non-deterministic approach.
Engineering EconomistENGINEERING, INDUSTRIAL-OPERATIONS RESEARCH & MANAGEMENT SCIENCE
CiteScore
2.00
自引率
0.00%
发文量
14
审稿时长
>12 weeks
期刊介绍:
The Engineering Economist is a refereed journal published jointly by the Engineering Economy Division of the American Society of Engineering Education (ASEE) and the Institute of Industrial and Systems Engineers (IISE). The journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment.
The journal seeks submissions in a number of areas, including, but not limited to: capital investment analysis, financial risk management, cost estimation and accounting, cost of capital, design economics, economic decision analysis, engineering economy education, research and development, and the analysis of public policy when it is relevant to the economic investment decisions made by engineers and technology managers.