Václav Sobotka, Hana Rudová

Uncertainty in Real-World Vehicle Routing (Extended Abstract)

Introduction

Uncertainty in real-world vehicle routing (extended abstract). Address uncertainty in real-world industrial vehicle routing problems. We extend deterministic heuristic solvers with efficient mechanisms to handle up to 1,000 customers.

Abstract

In our paper, we aim to address common sources of uncertainty in real-world industrial vehicle routing problems. By extending traditional deterministic heuristic solvers with easy-to-integrate, reusable, and computationally efficient mechanisms, we introduce reasoning about uncertainty while retaining the capability of scaling to problems with up to 1,000 customers. We formulate four such mechanisms, including standard chance constraints, two data manipulation methods, and a novel penalty-based method. These mechanisms are evaluated on both standard benchmarks and real-world industrial vehicle routing instances, and their key search-related properties are discussed. This extended abstract presents results previously published at the European Conference on Artificial Intelligence 2024.

Review

The paper, "Uncertainty in Real-World Vehicle Routing (Extended Abstract)," addresses a highly pertinent and challenging area in logistics and operations research: incorporating uncertainty into vehicle routing problems (VRPs). The authors aim to bridge the gap between theoretical VRP solutions and practical industrial applications, where uncertainty in travel times, customer demands, or availability is ubiquitous. The stated goal of extending traditional deterministic heuristic solvers with easy-to-integrate, reusable, and computationally efficient mechanisms is commendable, promising practical applicability and relevance for real-world scenarios that often struggle with the computational burden of robust or stochastic optimization approaches. Specifically, the work introduces four distinct mechanisms for reasoning about uncertainty. These include established methods like standard chance constraints, two data manipulation techniques, and a novel penalty-based method. The approach centers on augmenting existing heuristic solvers, which is a pragmatic choice given the need for scalability. The claim of retaining the capability to scale to problems with up to 1,000 customers is a significant technical achievement for uncertainty-aware VRPs. The mechanisms are rigorously evaluated on both standard benchmarks and real-world industrial instances, indicating a comprehensive empirical validation of their proposed methodologies and an insightful discussion of their key search-related properties. A key strength of this work lies in its emphasis on computational efficiency and scalability, which are critical for the adoption of uncertainty-aware solutions in industrial settings. The modular nature of the proposed extensions also suggests high reusability, which is valuable for practitioners. As an extended abstract, it effectively summarizes previously published results from the European Conference on Artificial Intelligence 2024, providing a concise overview of the problem, the proposed solutions, and their validation. This makes it a valuable summary for readers interested in robust and stochastic approaches to VRPs, particularly those focused on practical implementation and large-scale applications.

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