G.Kasuri Abhilashani, M. Ranathunga, A. Wijayanayake
{"title":"Minimising Last-Mile Delivery Cost and Vehicle Usage Through an Optimised Delivery Network Considering Customer-Preferred Time Windows","authors":"G.Kasuri Abhilashani, M. Ranathunga, A. Wijayanayake","doi":"10.1109/SCSE59836.2023.10215012","DOIUrl":null,"url":null,"abstract":"In the dynamic and developing e-commerce era, last-mile delivery has emerged as one of the critical operations among all. The last-mile delivery in the e-commerce industry is facing high costs due to a going economic crisis which led to fuel and other operating cost increments. To overcome this situation, the e-commerce industry needs to optimise vehicle delivery routing based on time windows to minimise the overall cost. Despite numerous studies on last-mile delivery, there is a paucity of studies on last-mile delivery optimisation considering the customer’s anticipated time windows. Therefore, this study has been conducted with the objective of optimising and minimising transportation costs and vehicle usage in last-mile delivery operations while meeting some practical requirements such as a variety of package types, package compatibility on different types of vehicles; the customer expected delivery time windows and a heterogeneous fleet of vehicles. After a careful literature review, this paper introduces a mathematical model to optimise last-mile delivery. The proposed mathematical model was simulated in SupplyChainGuru® modelling and simulation software. The study concluded that the overall last-mile delivery cost is minimised by about 22% while reducing the number of vehicles on the route, failed delivery package count and utilising the maximum possible capacity of vehicles while also increasing customer satisfaction by giving consumers a chance to select customer preferred time windows for package delivery. This cluster-based delivery will improve the routing of the e-commerce logistic supply chain and will serve as a platform for extending the cluster-based delivery process to other industries as well.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCSE59836.2023.10215012","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
In the dynamic and developing e-commerce era, last-mile delivery has emerged as one of the critical operations among all. The last-mile delivery in the e-commerce industry is facing high costs due to a going economic crisis which led to fuel and other operating cost increments. To overcome this situation, the e-commerce industry needs to optimise vehicle delivery routing based on time windows to minimise the overall cost. Despite numerous studies on last-mile delivery, there is a paucity of studies on last-mile delivery optimisation considering the customer’s anticipated time windows. Therefore, this study has been conducted with the objective of optimising and minimising transportation costs and vehicle usage in last-mile delivery operations while meeting some practical requirements such as a variety of package types, package compatibility on different types of vehicles; the customer expected delivery time windows and a heterogeneous fleet of vehicles. After a careful literature review, this paper introduces a mathematical model to optimise last-mile delivery. The proposed mathematical model was simulated in SupplyChainGuru® modelling and simulation software. The study concluded that the overall last-mile delivery cost is minimised by about 22% while reducing the number of vehicles on the route, failed delivery package count and utilising the maximum possible capacity of vehicles while also increasing customer satisfaction by giving consumers a chance to select customer preferred time windows for package delivery. This cluster-based delivery will improve the routing of the e-commerce logistic supply chain and will serve as a platform for extending the cluster-based delivery process to other industries as well.