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ID 115887
Author
Shang, Ke Southern University of Science and Technology|The University of Tokushima
Chan, Felix T. S. The Hong Kong Polytechnic University
Feng, Zuren Xi’an Jiaotong University
Ke, Liangjun Xi’an Jiaotong University
Content Type
Journal Article
Description
In this paper, the two-stage orienteering problem with stochastic weights is studied, where the first-stage problem is to plan a path under the uncertain environment and the second-stage problem is a recourse action to make sure that the length constraint is satisfied after the uncertainty is realized. First, we explain the recourse model proposed by Evers et al. (2014) and point out that this model is very complex. Then, we introduce a new recourse model which is much simpler with less variables and less constraints. Based on these two recourse models, we introduce two different two-stage robust models for the orienteering problem with stochastic weights. We theoretically prove that the two-stage robust models are equivalent to their corresponding static robust models under the box uncertainty set, which indicates that the two-stage robust models can be solved by using common mathematical programming solvers (e.g., IBM CPLEX optimizer). Furthermore, we prove that the two two-stage robust models are equivalent to each other even though they are based on different recourse models, which indicates that we can use a much simpler model instead of a complex model for practical use. A case study is presented by comparing the two-stage robust models with a one-stage robust model for the orienteering problem with stochastic weights. The numerical results of the comparative studies show the effectiveness and superiority of the proposed two-stage robust models for dealing with the two-stage orienteering problem with stochastic weights.
Journal Title
Complexity
ISSN
10762787
10990526
NCID
AA11038146
Publisher
Hindawi|Wiley
Volume
2020
Start Page
5649821
Published Date
2020-11-16
Rights
This is an open access article distributed under the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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DOI (Published Version)
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language
eng
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departments
Science and Technology