Abstract
The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of demand scenarios. The problem is formulated as a nonlinear stochastic optimization problem to minimize the hub installation costs, expected transportation costs and expected absolute deviation of transportation costs. To eliminate the nonlinearity, the equivalent linear problem is introduced. The expected absolute deviation is the robustness measure to derive the solution close to each scenario. The robust hub location is assumed to deliver the least costs difference across the scenarios. The number of scenarios increases size and complexity of the task. Therefore, the classical and improved Benders decomposition algorithms are applied to achieve the best computational performance. The numerical experiment on CAB and AP dataset presents the difference of resulting hub networks in stochastic and robust formulations. Furthermore, performance of two Benders decomposition strategies in comparison with Gurobi solver is assessed and discussed.
| Original language | English |
|---|---|
| Journal | Journal of Industrial Engineering International |
| DOIs | |
| Publication status | E-pub ahead of print - 1 Jan 2019 |
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Scopus subject areas
- Industrial and Manufacturing Engineering
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Robust uncapacitated multiple allocation hub location problem under demand uncertainty : minimization of cost deviations. / Lozkins, Aleksejs; Krasilnikov, Mikhail; Bure, Vladimir.
In: Journal of Industrial Engineering International, 01.01.2019.Research output
TY - JOUR
T1 - Robust uncapacitated multiple allocation hub location problem under demand uncertainty
T2 - minimization of cost deviations
AU - Lozkins, Aleksejs
AU - Krasilnikov, Mikhail
AU - Bure, Vladimir
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of demand scenarios. The problem is formulated as a nonlinear stochastic optimization problem to minimize the hub installation costs, expected transportation costs and expected absolute deviation of transportation costs. To eliminate the nonlinearity, the equivalent linear problem is introduced. The expected absolute deviation is the robustness measure to derive the solution close to each scenario. The robust hub location is assumed to deliver the least costs difference across the scenarios. The number of scenarios increases size and complexity of the task. Therefore, the classical and improved Benders decomposition algorithms are applied to achieve the best computational performance. The numerical experiment on CAB and AP dataset presents the difference of resulting hub networks in stochastic and robust formulations. Furthermore, performance of two Benders decomposition strategies in comparison with Gurobi solver is assessed and discussed.
AB - The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of demand scenarios. The problem is formulated as a nonlinear stochastic optimization problem to minimize the hub installation costs, expected transportation costs and expected absolute deviation of transportation costs. To eliminate the nonlinearity, the equivalent linear problem is introduced. The expected absolute deviation is the robustness measure to derive the solution close to each scenario. The robust hub location is assumed to deliver the least costs difference across the scenarios. The number of scenarios increases size and complexity of the task. Therefore, the classical and improved Benders decomposition algorithms are applied to achieve the best computational performance. The numerical experiment on CAB and AP dataset presents the difference of resulting hub networks in stochastic and robust formulations. Furthermore, performance of two Benders decomposition strategies in comparison with Gurobi solver is assessed and discussed.
KW - Absolute deviation
KW - Benders decomposition
KW - Hub location problem
KW - Pareto-optimal cuts
KW - Robust solution
KW - Stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=85071439193&partnerID=8YFLogxK
U2 - 10.1007/s40092-019-00329-9
DO - 10.1007/s40092-019-00329-9
M3 - Article
AN - SCOPUS:85071439193
JO - Journal of Industrial Engineering International
JF - Journal of Industrial Engineering International
SN - 1735-5702
ER -