221 research outputs found
INTEGRATED HUB LOCATION AND CAPACITATED VEHICLE ROUTING PROBLEM OVER INCOMPLETE HUB NETWORKS
Hub location problem is one of the most important topics encountered in transportation and logistics management. Along with the question of where to position hub facilities, how routes are determined is a further challenging problem. Although these two problems are often considered separately in the literature, here, in this study, the two are analyzed together. Firstly, we relax the restriction that a vehicle serves between each demand center and hub pair and propose a mixed-integer mathematical model for the single allocation p-hub median and capacitated vehicle routing problem with simultaneous pick-up and delivery. Moreover, while many studies in hub location problem literature assume that there is a complete hub network structure, we also relax this assumption and present the aforementioned model over incomplete hub networks. Computational analyses of the proposed models were conducted on various instances on the Turkish network. Results indicate that the different capacity levels of vehicles have an important impact on optimal hub locations, hub arc networks, and routing design
A Neural Benders Decomposition for the Hub Location Routing Problem
In this study, we propose an imitation learning framework designed to enhance
the Benders decomposition method. Our primary focus is addressing degeneracy in
subproblems with multiple dual optima, among which Magnanti-Wong technique
identifies the non-dominant solution. We develop two policies. In the first
policy, we replicate the Magnanti-Wong method and learn from each iteration. In
the second policy, our objective is to determine a trajectory that expedites
the attainment of the final subproblem dual solution. We train and assess these
two policies through extensive computational experiments on a network design
problem with flow subproblem, confirming that the presence of such learned
policies significantly enhances the efficiency of the decomposition process
Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU
A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to
solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses
binary and integer encoding and genetic operators adapted to this problem. Our
GA is improved by generated initial solution with hubs located at middle nodes.
The obtained experimental results are compared with the best known solutions on
all benchmarks on instances up to 1000 nodes. Furthermore, we solve our own
randomly generated instances up to 6000 nodes. Our approach outperforms most
well-known heuristics in terms of solution quality and time execution and it
allows hitherto unsolved problems to be solved
Robust intermodal hub location under polyhedral demand uncertainty
In this study, we consider the robust uncapacitated multiple allocation p-hub median problem under polyhedral demand uncertainty. We model the demand uncertainty in two different ways. The hose model assumes that the only available information is the upper limit on the total flow adjacent at each node, while the hybrid model additionally imposes lower and upper bounds on each pairwise demand. We propose linear mixed integer programming formulations using a minmax criteria and devise two Benders decomposition based exact solution algorithms in order to solve large-scale problems. We report the results of our computational experiments on the effect of incorporating uncertainty and on the performance of our exact approaches. © 2016 Elsevier Ltd
A new formulation and branch-and-cut method for single-allocation hub location problems
A new compact formulation for uncapacitated single-allocation hub location problems with fewer variables than the previous Integer Linear Programming formulations in the literature is introduced. Our formulation works even with costs not based on distances and not satisfying triangle inequality. Moreover, costs can be given in aggregated or disaggregated way. Different families of valid inequalities that strengthen the formulation are developed and a branch-and-cut algorithm based on a relaxed version of the formulation is designed, whose restrictions are inserted in a cut generation procedure together with two sets of valid inequalities. The performance of the proposed methodology is tested on well-known hub location data sets and compared to the most recent and efficient exact algorithms for single-allocation hub location problems. Extensive computational results prove the efficiency of our methodology, that solves large-scale instances in very competitive times
Estudio computacional de procedimientos de descomposición automática de modelos fundamentales de localización de hubs
Tesis (Ingeniero Civil Industrial)Existen diferentes problemas de localización de hubs. En la literatura no se encontraron estudios sobre el comportamiento de estos problemas, utilizando el método de la descomposición de Benders. Se presentan diferentes problemas de localización de hubs para ser resueltos por el método de Descomposición de Benders en el solver CPLEX 12.7, se busca caracterizar el método para diferentes instancias y parámetros. Midiendo el rendimiento de cada modelo.There are different problems of hubs location. in the literature there is not studies about the behavior of hubs location, using Benders Decomposition. This paper present different hubs location problems resolved by Benders Decomposition using the optimizer CPLEX 12.7. It is sought to characterize the method for different instances and parameters where performance of each model is measure
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