Abstract
The recent increase in online orders in e-commerce leads to logistical challenges such as low hit rates (proportion of successful deliveries). We consider last-mile vehicle routing and scheduling problems in which customer presence probability data are taken into account. The aim is to reduce the expected cost resulting from low hit rates by considering both routing and scheduling decisions simultaneously in the planning phase. We model the problem and solve it by the means of an adaptive large neighborhood search metaheuristic which iterates between the routing and scheduling components of the problem. Computational experiments indicate that using customer-related presence data significantly can yield savings as large as 40% in system-wide costs compared with those of traditional vehicle routing solutions.
Original language | English |
---|---|
Article number | 102263 |
Journal | Transportation Research Part E: Logistics and Transportation Review |
Volume | 148 |
DOIs | |
Publication status | Published - 1 Apr 2021 |
Bibliographical note
Funding Information:This research was partly funded by the Canadian Natural Sciences and Engineering Research Council under grant 2015-06189 . This research was also funded by The Dutch Research Council (NWO) Data2Move project under grant 628.009.013 . These supports are greatly acknowledged.
Publisher Copyright:
© 2021 The Author(s)