The enriched median routing problem and its usefulness in practice

Dylan Huizing*, Rob van der Mei, Guido Schäfer, Sandjai Bhulai

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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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 languageEnglish
Article number108063
Pages (from-to)1-14
Number of pages14
JournalComputers and Industrial Engineering
Volume168
Early online date10 Mar 2022
DOIs
Publication statusPublished - 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.

Keywords

  • Combined planning
  • Emergency logistics
  • Location
  • Routing
  • Simulation study

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