2,053 research outputs found
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
Variable Neighborhood Search for Solving the Capacitated Single Allocation Hub Location Problem
In this paper a Variable Neighborhood Search (VNS) algorithm for solving the Capacitated Single Allocation Hub Location Problem (CSAHLP) is presented. CSAHLP consists of two subproblems; the first is choosing a set of hubs from all nodes in a network, while the other comprises finding the optimal allocation of non-hubs to hubs when a set of hubs is already known. The VNS algorithm was used for the first subproblem, while the CPLEX solver was used for the second. Computational results demonstrate that the proposed algorithm has reached optimal solutions on all 20 test instances for which optimal solutions are known, and this in short computational time
Network hub locations problems: the state of the art
Cataloged from PDF version of article.Hubs are special facilities that serve as switching, transshipment and sorting points in many-to-many distribution systems. The hub location problem is concerned with locating hub facilities and allocating demand nodes to hubs in order to route the traffic between origin-destination pairs. In this paper we classify and survey network hub location models. We also include some recent trends on hub location and provide a synthesis of the literature. (C) 2007 Elsevier B.V. All rights reserved
Air Taxi Skyport Location Problem for Airport Access
Witnessing the rapid progress and accelerated commercialization made in
recent years for the introduction of air taxi services in near future across
metropolitan cities, our research focuses on one of the most important
consideration for such services, i.e., infrastructure planning (also known as
skyports). We consider design of skyport locations for air taxis accessing
airports, where we present the skyport location problem as a modified
single-allocation p-hub median location problem integrating choice-constrained
user mode choice behavior into the decision process. Our approach focuses on
two alternative objectives i.e., maximizing air taxi ridership and maximizing
air taxi revenue. The proposed models in the study incorporate trade-offs
between trip length and trip cost based on mode choice behavior of travelers to
determine optimal choices of skyports in an urban city. We examine the
sensitivity of skyport locations based on two objectives, three air taxi
pricing strategies, and varying transfer times at skyports. A case study of New
York City is conducted considering a network of 149 taxi zones and 3 airports
with over 20 million for-hire-vehicles trip data to the airports to discuss
insights around the choice of skyport locations in the city, and demand
allocation to different skyports under various parameter settings. Results
suggest that a minimum of 9 skyports located between Manhattan, Queens and
Brooklyn can adequately accommodate the airport access travel needs and are
sufficiently stable against transfer time increases. Findings from this study
can help air taxi providers strategize infrastructure design options and
investment decisions based on skyport location choices.Comment: 25 page
A Genetic Algorithm Approach for the Capacitated Single Allocation P-Hub Median Problem
In this paper the Capacitated Single Allocation p-Hub Median Problem (CSApHMP) is considered. This problem has a wide range of applications within the design of telecommunication and transportation systems. A heuristic method, based on a genetic algorithm (GA) approach, is proposed for solving the CSApHMP. The described algorithm uses binary encoding and modified genetic operators. The caching technique is also implemented in the GA in order to improve its effectiveness. Computational experiments demonstrate that the GA method quickly reaches optimal solutions for hub instances with up to 50 nodes. The algorithm is also benchmarked on large scale hub instances with up to 200 nodes that are not solved to optimality so far
Experimental Evaluation of Meta-Heuristics for Multi-Objective Capacitated Multiple Allocation Hub Location Problem
Multi-objective capacitated multiple allocation hub location problem (MOCMAHLP) is a variation of classic
hub location problem, which deals with network design, considering both the number and the location
of the hubs and the connections between hubs and spokes, as well as routing of flow on the network.
In this study, we offer two meta-heuristic approaches based on the non-dominated sorting genetic algorithm
(NSGA-II) and archived multi-objective simulated annealing method (AMOSA) to solve
MOCMAHLP. We attuned AMOSA based approach to obtain feasible solutions for the problem and developed
five different neighborhood operators in this approach. Moreover, for NSGA-II based approach, we
developed two novel problem-specific mutation operators. To statistically analyze the behavior of both
algorithms, we conducted experiments on two well-known data sets, namely Turkish and Australian
Post (AP). Hypervolume indicator is used as the performance metric to measure the effectiveness of both
approaches on the given data sets. In the experimental study, thorough tests are conducted to fine-tune
the proposed mutation types for NSGA-II and proposed neighborhood operators for AMOSA. Fine-tuning
tests reveal that for NSGA-II, mutation probability does not have a real effect on Turkish data set, whereas
lower mutation probabilities are slightly better for AP data set. Moreover, among the AMOSA based
neighborhood operators, the one which adds/removes a specific number of links according to temperature
(NS-5) performs better than the others for both data sets. After analyzing different operators for both
algorithms, a comparison between our NSGA-II based and AMOSA based approaches is performed with
the best settings. As a result, we conclude that both of our algorithms are able to find feasible solutions
of the problem. Moreover, NSGA-II performs better for larger, whereas AMOSA performs better for smaller
size networks
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