2693. Choice-driven periodic vehicle routing and pricing
Invited abstract in session TB-56: Multi-Period Vehicle Routing Problems, stream Vehicle Routing and Logistics.
Tuesday, 10:30-12:00Room: Liberty 1.11
Authors (first author is the speaker)
| 1. | Chenghua Yang
|
| Mechanical Engineering, Delft University of Technology | |
| 2. | Jie Gao
|
| Transport & Planning, Delft University of Technology (TU Delft) | |
| 3. | Bilge Atasoy
|
| Maritime and Transport Technology, Delft University of Technology |
Abstract
This study addresses the tactical decision-making challenges faced by Logistics Service Providers (LSPs) that are periodically supplying heterogeneous business customers (e.g., restaurants, hotels, cafes). Customers exhibit distinct preferences for delivery service characteristics, such as price, frequency, time of day, and time window length. LSPs aim to maximize their profit and their profitability relies on personalized service recommendations and reasonable pricing to attract customers while maintaining cost-efficient operations. Leveraging customer information at a disaggregated level enhances the accuracy of service customization and pricing strategies. To this end, we propose a choice-driven optimization framework that integrates advanced discrete choice models into the Periodic Routing and Pricing problem to optimize the system across multiple objectives.
We formulate the problem as a stochastic bi-level optimization model, where the LSP (leader) anticipates customer (follower) reactions based on a random utility maximization model. To handle the random term in the utility function, we apply a sample average approximation and reformulate the problem as a single-level mixed-integer linear program. Experimental results demonstrate that explicitly considering customer behavior at the disaggregated level prevents profit overestimation and helps improve service levels and overall profitability.
Keywords
- Transportation
- Vehicle Routing
- Behavioural OR
Status: accepted
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