EURO 2024 Copenhagen
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1862. Explorable Uncertainty in Routing

Invited abstract in session TB-39: Stochastic Models in Logistics, stream Stochastic Modelling.

Tuesday, 10:30-12:00
Room: 35 (building: 306)

Authors (first author is the speaker)

1. Caroline Spieckermann
TUM School of Management, Technical University of Munich
2. Christoph Kerscher
Logistics and Supply Chain Management, Technical University of Munich
3. Stefan Minner
TUM School of Management, Technische Universität München

Abstract

Planning in logistics and transportation is oftentimes complicated by a high degree of uncertainty about the actual travel distances, times, or costs. While stochastic optimization is concerned with making optimal decisions under such uncertainty, it disregards that in many practical applications, uncertainty can be reduced upfront through research and tests, also known as "explorable uncertainty". However, while uncertainty reduction through exploration can improve decision-making, it oftentimes comes at a cost, and one needs to balance exploration costs and solution quality. We study the vehicle routing problem with time windows (VRPTW) and stochastic travel times where uncertainty about travel times can be reduced by making queries to a traffic data provider while respecting an overall querying budget. This converts the stochastic VRPTW into a partially deterministic problem that we solve via point-based approximation and sample average approximation to deal with the remaining uncertainty. We present different methods to make good and fast querying decisions based on statistical features and learning and show their effectiveness in an extensive numerical study. By assuming different degrees of uncertainty, correlations, and time-window restrictions, we give detailed insights into the value of uncertainty exploration in routing.

Keywords

Status: accepted

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