Your browser doesn't support javascript.
The adoption of self-driving delivery robots in last mile logistics.
Chen, Cheng; Demir, Emrah; Huang, Yuan; Qiu, Rongzu.
  • Chen C; School of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Demir E; Panalpina Centre for Manufacturing and Logistics Research, Cardiff Business School, Cardiff University, Cardiff, United Kingdom.
  • Huang Y; Logistics and Operations Management, Cardiff Business School, Cardiff University, Cardiff, United Kingdom.
  • Qiu R; Logistics and Operations Management, Cardiff Business School, Cardiff University, Cardiff, United Kingdom.
Transp Res E Logist Transp Rev ; 146: 102214, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1039579
ABSTRACT
Covid-19, the global pandemic, has taught us the importance of contactless delivery service and robotic automation. Using self-driving delivery robots can provide flexibility for on-time deliveries and help better protect both driver and customers by minimizing contact. To this end, this paper introduces a new vehicle routing problem with time windows and delivery robots (VRPTWDR). With the help of delivery robots, considerable operational time savings can be achieved by dispatching robots to serve nearby customers while a driver is also serving a customer. We provide a mathematical model for the VRPTWDR and investigate the challenges and benefits of using delivery robots as assistants for city logistics. A two-stage matheurisitic algorithm is developed to solve medium scale VRPTWDR instances. Finally, results of computational experiments demonstrate the value of self-driving delivery robots in urban areas by highlighting operational limitations on route planning.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Transp Res E Logist Transp Rev Year: 2021 Document Type: Article Affiliation country: J.tre.2020.102214

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Transp Res E Logist Transp Rev Year: 2021 Document Type: Article Affiliation country: J.tre.2020.102214