Two-stage robust optimization for orienteering problem with stochastic weights
Ke Shang, Felix T.S. Chan, Stephen Karungaru, Kenji Terada, Zuren, Feng, Liangjun Ke

TL;DR
This paper introduces two efficient two-stage robust optimization models for the orienteering problem with stochastic weights, demonstrating their effectiveness and theoretical equivalence to static models through a case study.
Contribution
It proposes novel two-stage robust models for OPSW based on different recourse approaches, with proven theoretical equivalence to static models and improved computational efficiency.
Findings
Two-stage robust models outperform one-stage models in case studies.
The models are theoretically equivalent to static robust models under box uncertainty.
The second recourse model is computationally more efficient.
Abstract
In this paper, the two-stage orienteering problem with stochastic weights (OPSW) is considered, where the first-stage problem is to plan a path under the uncertain environment and the second-stage problem is recourse action to make sure that the length constraint is satisfied after the uncertainty is realized. Two recourse models are introduced based on two different uncertainty realization ways, one is based on sequentially realized weights which leads to the recourse model proposed by Evers et al. (2014) and the other is based on concurrently realized weights which leads to a new recourse model with less variables and less constraints and is computationally more efficient. Subsequently two two-stage robust models are introduced for OPSW based on the two different recourse models, and the relationships between the two-stage robust models and their corresponding static robsut models are…
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Taxonomy
TopicsOptimization and Mathematical Programming · Advanced Multi-Objective Optimization Algorithms · Water resources management and optimization
