{"title":"Planning delivery-by-drone micro-fulfilment centres","authors":"J. S. Lamb , S. C. Wirasinghe , N. M. Waters","doi":"10.1080/23249935.2022.2107729","DOIUrl":null,"url":null,"abstract":"<div><p>Delivery drones are a disruptive technology that is spurring logistics system change, such as the adoption of urban micro-fulfilment centres (MFCs). In this paper, we develop and implement a two-stage continuum approximation (CA) model of this disruptive system in a geographic information system. The model includes common CA techniques at a local level to minimise cost, and then these local solutions are used in a second stage regional location-allocation multiple knapsack problem. We then compare the drone MFC system to a traditional delivery-by-van system and investigate potential cost or emissions savings by adjusting time-window demand, logistical sprawl, electric van alternatives, and MFC emissions. Furthermore, we conduct a sensitivity analysis to show that uncertainty in demand and effective storage density both significantly influence the number of MFCs selected and benchmark our model against commercial solvers. This methodology may also be further developed and applied to other new delivery vehicle modes.</p></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"20 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportmetrica A-Transport Science","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S2324993523000052","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
Abstract
Delivery drones are a disruptive technology that is spurring logistics system change, such as the adoption of urban micro-fulfilment centres (MFCs). In this paper, we develop and implement a two-stage continuum approximation (CA) model of this disruptive system in a geographic information system. The model includes common CA techniques at a local level to minimise cost, and then these local solutions are used in a second stage regional location-allocation multiple knapsack problem. We then compare the drone MFC system to a traditional delivery-by-van system and investigate potential cost or emissions savings by adjusting time-window demand, logistical sprawl, electric van alternatives, and MFC emissions. Furthermore, we conduct a sensitivity analysis to show that uncertainty in demand and effective storage density both significantly influence the number of MFCs selected and benchmark our model against commercial solvers. This methodology may also be further developed and applied to other new delivery vehicle modes.
无人机送货是一项颠覆性技术，它正在推动物流系统的变革，例如城市微型配送中心（MFC）的采用。在本文中，我们在地理信息系统中为这一颠覆性系统开发并实施了一个两阶段连续逼近（CA）模型。该模型包括局部层面的常用 CA 技术，以最大限度地降低成本，然后将这些局部解决方案用于第二阶段的区域位置分配多重背包问题。然后，我们将无人机多式联运系统与传统的货车送货系统进行比较，并通过调整时间窗口需求、物流扩张、电动货车替代品和多式联运系统的排放量来研究潜在的成本或排放量节省。此外，我们还进行了敏感性分析，以表明需求和有效存储密度的不确定性都会对所选 MFC 的数量产生重大影响，并将我们的模型与商业求解器进行比较。这种方法还可进一步开发并应用于其他新型配送车辆模式。
期刊介绍：
Transportmetrica A provides a forum for original discourse in transport science. The international journal''s focus is on the scientific approach to transport research methodology and empirical analysis of moving people and goods. Papers related to all aspects of transportation are welcome. A rigorous peer review that involves editor screening and anonymous refereeing for submitted articles facilitates quality output.
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