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2305. Pricing and bundling decisions considering driver behavior in crowdsourced delivery
Invited abstract in session MB-52: Combinatorial optimization approaches for freight deliveries, stream Combinatorial Optimization.
Monday, 10:30-12:00Room: 8003 (building: 202)
Authors (first author is the speaker)
| 1. | Alim Buğra Çınar
|
| Operations Analytics, Vrije Universiteit Amsterdam | |
| 2. | Claudia Archetti
|
| Università degli Studi di Brescia | |
| 3. | Wout Dullaert
|
| Operation analytics, Vrije Universiteit Amsterdam | |
| 4. | Markus Leitner
|
| Department of Operations Analytics, Vrije Universiteit Amsterdam | |
| 5. | Stefan Waldherr
|
| Vrije Universiteit Amsterdam |
Abstract
Crowdsourced delivery utilizes the services of independent actors. As opposed to traditional modes of delivery, availability and acceptance decisions of crowdshippers are uncertain and cannot be fully controlled by an operator. We consider a setting in which an operator groups tasks into bundles and in which the resulting bundles are offered to crowdshippers in exchange for some compensation. Uncertainty in the crowdshippers' behavior who may accept or reject offers is considered via (individual) acceptance probabilities. We consider generic probability functions whose main parameters are the compensation offered, the number of tasks in a bundle, and the total detour to deliver a bundle. The objective of the resulting optimization problem is to minimize the expected total cost of delivery. We propose a mixed-integer non-linear programming (MINLP) formulation that simultaneously decides how to group tasks into bundles, which bundles are offered to which crowdshipper, and the compensation offered for each bundle. We show that this MINLP can be reformulated as a mixed-integer linear program with an exponential number of variables. We present a column generation algorithm for solving instances of the latter whose pricing subproblem corresponds to an elementary shortest path problem with resource constraints (ESPPRC) and a nonlinear objective function. Our experiments show that the algorithm is capable of solving large instances.
Keywords
- Column Generation
- Logistics
- Transportation
Status: accepted
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