TY - JOUR
ID - 676902
TI - Robust uncapacitated multiple allocation hub location problem under demand uncertainty: minimization of cost deviations
JO - Journal of Industrial Engineering, International
JA - JIEI
LA - en
SN - 1735-5702
AU - Lozkins, Aleksejs
AU - Krasilnikov, Mikhail
AU - Bure, Vladimir
AD - Department of Mathematical Game Theory and Statistical Decisions, Saint Petersburg State University, 7-9, Universitetskaya nab., St. Petersburg, Russian Federation, 199034
AD - Department of Mathematical optimization and Modeling, Ltd. BIA-Technologies, 94, Moskovskiy avenue, St. Petersburg, Russian Federation, 196084
Y1 - 2020
PY - 2020
VL - 16
IS - 3
SP -
EP -
KW - Hub location problem
KW - Stochastic programming
KW - Absolute deviation · Robust solution
KW - Benders decomposition
KW - Pareto
KW - optimal cuts
DO -
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.
UR - http://jiei.azad.ac.ir/article_676902.html
L1 - http://jiei.azad.ac.ir/article_676902_da96e738ba7f248b6fb64ddac6143d2a.pdf
ER -