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3565. The adoption of self-driving robots in last mile healthcare logistics
Invited abstract in session MC-29: Vehicle routing I, stream Combinatorial Optimization.
Monday, 12:30-14:00Room: 157 (building: 208)
Authors (first author is the speaker)
| 1. | Somayeh Allahyari
|
| Business School, The University of Birmingham | |
| 2. | Gu Pang
|
| Business School, The University of Birmingham | |
| 3. | Yufeng Zhang
|
| Business School, The University of Birmingham | |
| 4. | Keru Duan
|
| Business School, The University of Birmingham |
Abstract
The adoption of self-driving robots across various delivery applications presents a transformative shift in urban logistics, offering enhanced efficiency, agility for fast and on-time deliveries, sustainability through zero emissions, and reduced human contact. The onset of the COVID-19 pandemic expedited the execution of robot delivery roll-out strategies, leading to market size growth and attracting new competitors. This study investigates the integration of autonomous delivery/collection robots into last-mile logistics for synchronised collection of medical samples in the healthcare sector, where vans are equipped with self-driving robots. A multi-echelon network topology is adopted by leveraging insights from the UK public health service's (known as NHS) hub and spoke network strategy which prioritises faster and more reliable screening test results, including for cancer. The study addresses the new operational challenges associated with implementing this new self-driving robot technology, focusing on routing and scheduling logistics problems. To tackle these challenges, a mixed-integer linear programming model and an Adaptive Large Neighbourhood Search (ALNS) metaheuristic algorithm are developed. Experimental results demonstrate the effectiveness of both the model and the algorithm in optimising last-mile service in the healthcare sector, shedding light on the potential of self-driving robots to revolutionise healthcare logistics.
Keywords
- Combinatorial Optimization
- Logistics
- Health Care
Status: accepted
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