Instances for Optimal Taxes and Subsidies to Incentivize Modal Shift for Inner-city Freight Transport
Description
This dataset supports the study "Optimal Taxes and Subsidies to Incentivize Modal Shift for Inner-city Freight Transport". It consists of instances generated for the Pickup and Delivery Problem with Time Windows scheduled line services, following the problem proposed by Ghilas et al. (2018). Each instances consists of number of vehicles with capacity and its depot, requests, fixed scheduled line service departure time, node location and time window. The dataset is organized according to the corresponding sections of the study: Section 6: Contains artificially generated instances based on the framework of Ghilas et al. (2018). These are grouped into five distinct folders, as illustrated and explained in our study. Section 7: Case Study: For the real-world case study, we adopt the Berlin dataset of 5000 requests from Sartori and Buriol (2020). Additionally, a detailed description of the instance sets is provided in "Description.txt" to facilitate understanding and reproducibility. References: 1. Tundulyasaree, K., Martin, L., van Lieshout, R. N., & Van Woensel, T. (2025). Optimal taxes and subsidies to incentivize modal shift for inner-city freight transport. arXiv preprint arXiv:2501.09467. 2. Ghilas, V., Cordeau, J. F., Demir, E., & Van Woensel, T. (2018). Branch-and-price for the pickup and delivery problem with time windows and scheduled lines. Transportation Science, 52(5), 1191–1210. 3. Sartori, C. S., & Buriol, L. S. (2020). A study on the pickup and delivery problem with time windows: Matheuristics and new instances. Computers and Operations Research, 124, 105065.
Files
Steps to reproduce
Section 6 – Artificial Instances: These instances are generated following the procedure outlined by Ghilas et al. (2018), with a minor modification: depot and scheduled line (SL) locations are adjusted to overlap. The data is organized into five folders, as illustrated in the study. Section 7 – Case Study Instances: For the real-world case study, we sample requests from the largest instance provided by Sartori and Buriol (2020) in the Berlin scenario. To enhance realism, we incorporate actual depot locations from a logistics company and use real station data from the Berlin S-Bahn network.
Institutions
- Technische Universiteit Eindhoven Faculteit Industrial Engineering and Innovation Sciences