Abstract
Emergency response fleets often have to simultaneously perform two types of tasks: (1) urgent tasks requiring immediate action, and (2) non-urgent preventive maintenance tasks that can be scheduled upfront. In Huizing et al. (2020), Huizing et al. proposed the Median Routing Problem (MRP) to optimally schedule agents to a given set of non-urgent tasks, such that the response time for urgent tasks remains minimal. They proposed both an exact MILP-solution and a fast, scalable and accurate heuristic. However, when implementing the MRP-solution in a real-life pilot with a Dutch railway provider, we found that the model needed to be extended by including additional practical objectives and constraints. Therefore, in this paper, we extend the MRP to the so-called Enriched Median Routing Problem (E-MRP), making the model much better aligned with considerations from practice. Accordingly, we extend the MRP-based solutions to the E-MRP. This allows us to compare the performance of our proposed E-MRP solutions to performance obtained in the current operational practice of our partnering railway infrastructure company. We conclude that the E-MRP solution leads to a strong reduction in emergency response times compared to current practice by smartly scheduling the same volumes of non-urgent tasks.
| Original language | English |
|---|---|
| Article number | 108063 |
| Pages (from-to) | 1-14 |
| Number of pages | 14 |
| Journal | Computers and Industrial Engineering |
| Volume | 168 |
| Early online date | 10 Mar 2022 |
| DOIs | |
| Publication status | Published - Jun 2022 |
Bibliographical note
Funding Information:We thank our railway industry partner for co-funding this research and for supplying realistic case study data.
Publisher Copyright:
© 2022
Funding
We thank our railway industry partner for co-funding this research and for supplying realistic case study data.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Combined planning
- Emergency logistics
- Location
- Routing
- Simulation study
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