{"title":"Cargo Operations of Express Air","authors":"Kexin Wu, Jiarong Chen","doi":"10.26855/ea.2023.08.012","DOIUrl":null,"url":null,"abstract":"This report focuses on solving the cargo transportation problem for Express Air, an air express company operating among three airports. The objective is to minimize costs while meeting the daily demand for cargo transportation between each origin-destination airport. A minimum cost optimization model is employed to find the optimal schedule. Sensibility checks are performed, and the results are analyzed, including various scenarios using the developed models. The mathematical model includes variables representing cargo amounts, repositioning aircraft, staying aircraft, and cargo left on the ground. The objective function aims to minimize the total cost of cargo left on the ground and repositioning aircraft. Constraints ensure the balance of aircraft and cargo flows and account for demand and cargo left from previous days. The optimal solution yields a total cost of 17,925, with repositioning aircraft accounting for 84.38% of the cost. Increasing the fleet size reduces costs, and the model can be applied to supply chain systems and on-line shopping logistics.","PeriodicalId":72384,"journal":{"name":"Biomedical engineering advances","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical engineering advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26855/ea.2023.08.012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This report focuses on solving the cargo transportation problem for Express Air, an air express company operating among three airports. The objective is to minimize costs while meeting the daily demand for cargo transportation between each origin-destination airport. A minimum cost optimization model is employed to find the optimal schedule. Sensibility checks are performed, and the results are analyzed, including various scenarios using the developed models. The mathematical model includes variables representing cargo amounts, repositioning aircraft, staying aircraft, and cargo left on the ground. The objective function aims to minimize the total cost of cargo left on the ground and repositioning aircraft. Constraints ensure the balance of aircraft and cargo flows and account for demand and cargo left from previous days. The optimal solution yields a total cost of 17,925, with repositioning aircraft accounting for 84.38% of the cost. Increasing the fleet size reduces costs, and the model can be applied to supply chain systems and on-line shopping logistics.