scholarly journals A two-stage robust hub location problem with accelerated Benders decomposition algorithm

2021 ◽  
pp. 1-23
Author(s):  
Reza Rahmati ◽  
Mahdi Bashiri ◽  
Erfaneh Nikzad ◽  
Ali Siadat
Keyword(s):  
Benders Decomposition ◽  
Location Problem ◽  
Decomposition Algorithm ◽  
Hub Location ◽  
Hub Location Problem ◽  
Two Stage ◽  
Benders Decomposition Algorithm

Benders decomposition applied to a robust multiple allocation incomplete hub location problem

2018 ◽  
Vol 89 ◽  
pp. 31-50 ◽  
Author(s):  
Elisangela Martins de Sá ◽  
Reinaldo Morabito ◽  
Ricardo Saraiva de Camargo
Keyword(s):  
Benders Decomposition ◽  
Location Problem ◽  
Hub Location ◽  
Hub Location Problem ◽  
Multiple Allocation

scholarly journals A loose Benders decomposition algorithm for approximating two-stage mixed-integer recourse models

2020 ◽  
Author(s):  
Niels van der Laan ◽  
Ward Romeijnders
Keyword(s):  
Error Bounds ◽  
Benders Decomposition ◽  
Decomposition Algorithm ◽  
Mixed Integer ◽  
Large Problem ◽  
Two Stage ◽  
Convex Approximations ◽  
Integer Recourse ◽  
Benders Decomposition Algorithm ◽  
Problem Instances

Abstract We propose a new class of convex approximations for two-stage mixed-integer recourse models, the so-called generalized alpha-approximations. The advantage of these convex approximations over existing ones is that they are more suitable for efficient computations. Indeed, we construct a loose Benders decomposition algorithm that solves large problem instances in reasonable time. To guarantee the performance of the resulting solution, we derive corresponding error bounds that depend on the total variations of the probability density functions of the random variables in the model. The error bounds converge to zero if these total variations converge to zero. We empirically assess our solution method on several test instances, including the SIZES and SSLP instances from SIPLIB. We show that our method finds near-optimal solutions if the variability of the random parameters in the model is large. Moreover, our method outperforms existing methods in terms of computation time, especially for large problem instances.

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