Publication Type

Conference Proceeding Article

Publication Date

10-2017

Abstract

This paper considers the problem of matching multiple shippers and multi-transporters for pickups and drop-offs, where the goal is to select a subset of group jobs (shipper bids) that maximizes profit. This is the underlying winner determination problem in an online auction-based vehicle sharing platform that matches transportation demand and supply, particularly in a B2B last-mile setting. Each shipper bid contains multiple jobs, and each job has a weight, volume, pickup location, delivery location and time window. On the other hand, each transporter bid specifies the vehicle capacity, available time periods, and a cost structure. This double-sided auction will be cleared by the platform to find a profit-maximizing match and corresponding routes while respecting shipper and transporter constraints. Compared to the classical pickup-and-delivery problem, a key challenge is the dependency among jobs, more precisely, all jobs within a shipper bid must either be accepted or rejected together and jobs within a bid may be assigned to different transporters. We formulate the mathematical model and propose an Adaptive Large Neighborhood Search approach to solve the problem heuristically. We also derive management insights obtained from our computational experiments.

Keywords

Pickup-and-delivery problem with jobs dependency, Winner determination problem, Logistics

Discipline

Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms

Research Areas

Intelligent Systems and Decision Analytics

Publication

Computational Logistics: 8th International Conference, ICCL 2017, Southampton, October 18-20, 2017: Proceedings

Volume

10572

First Page

127

Last Page

142

ISBN

9783319684963

Identifier

10.1007/978-3-319-68496-3_9

Publisher

Springer

City or Country

Cham

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1007/978-3-319-68496-3_9