63 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
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
Hub Covering Location Problem Considering Queuing and Capacity Constraints
In this paper, a hub covering location problem is considered. Hubs, which are the most congested part of a network, are modeled as M/M/C queuing system and located in placeswhere the entrance flows are more than a predetermined value.A fuzzy constraint is considered in order to limit the transportation time between all origin-destination pairs in the network.On modeling, a nonlinear mathematical program is presented.Then, the nonlinear constraints are convertedto linear ones.Due to the computational complexity of the problem,genetic algorithm (GA),particle swarm optimization (PSO)based heuristics, and improved hybrid PSO are developedto solve the problem. Since the performance of the given heuristics is affected by the corresponding parameters of each, Taguchi method is appliedin order to tune the parameters. Finally,the efficiency ofthe proposed heuristicsis studied while designing a number of test problems with different sizes.The computational results indicated the greater efficiency of the heuristic GA compared to the other methods for solving the proble
A metaheuristic and simheuristic approach for the p-Hub median problem from a telecommunication perspective
Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Avanços recentes no setor das telecomunicações oferecem grandes oportunidades para cidadãos
e organizações em um mundo globalmente conectado, ao mesmo tempo em que surge um vasto
número de desafios complexos que os engenheiros devem enfrentar. Alguns desses desafios podem
ser modelados como problemas de otimização. Alguns exemplos incluem o problema de alocação
de recursos em redes de comunicações, desenho de topologias de rede que satisfaça determinadas
propriedades associadas a requisitos de qualidade de serviço, sobreposição de redes multicast e
outros recursos importantes para comunicação de origem a destino.
O primeiro objetivo desta tese é fornecer uma revisão sobre como as metaheurísticas têm sido
usadas até agora para lidar com os problemas de otimização associados aos sistemas de telecomunicações, detectando as principais tendências e desafios. Particularmente, a análise enfoca os
problemas de desenho, roteamento e alocação de recursos. Além disso, devido á natureza desses
desafios, o presente trabalho discute como a hibridização de metaheurísticas com metodologias
como simulação pode ser empregada para ampliar as capacidades das metaheurísticas na resolução
de problemas de otimização estocásticos na indústria de telecomunicações.
Logo, é analisado um problema de otimização com aplicações práticas para redes de telecomunica
ções: o problema das p medianas não capacitado em que um número fixo de hubs tem
capacidade ilimitada, cada nó não-hub é alocado para um único hub e o número de hubs é conhecido
de antemão, sendo analisado em cenários determinísticos e estocásticos. Dada a sua variedade
e importância prática, o problema das p medianas vem sendo aplicado e estudado em vários contextos.
Seguidamente, propõem-se dois algoritmos imune-inspirados e uma metaheurística de dois estágios, que se baseia na combinação de técnicas tendenciosas e aleatórias com uma estrutura de
busca local iterada, além de sua integração com a técnica de simulação de Monte Carlo para resolver
o problema das p medianas. Para demonstrar a eficiência dos algoritmos, uma série de testes
computacionais é realizada, utilizando instâncias de grande porte da literatura. Estes resultados
contribuem para uma compreensão mais profunda da eficácia das metaheurísticas empregadas
para resolver o problema das p medianas em redes pequenas e grandes. Por último, uma aplicaçã
o ilustrativa do problema das p medianas é apresentada, bem como alguns insights sobre novas
possibilidades para ele, estendendo a metodologia proposta para ambientes da vida real.Recent advances in the telecommunication industry o er great opportunities to citizens and
organizations in a globally-connected world, but they also arise a vast number of complex challenges
that decision makers must face. Some of these challenges can be modeled as optimization
problems. Examples include the framework of network utility maximization for resource allocation
in communication networks, nding a network topology that satis es certain properties associated
with quality of service requirements, overlay multicast networks, and other important features for
source to destination communication.
First, this thesis provides a review on how metaheuristics have been used so far to deal with
optimization problems associated with telecommunication systems, detecting the main trends and
challenges. Particularly the analysis focuses on the network design, routing, and allocation problems.
In addition, due to the nature of these challenges, this work discusses how the hybridization
of metaheuristics with methodologies such as simulation can be employed to extend the capabilities
of metaheuristics when solving stochastic optimization problems.
Then, a popular optimization problem with practical applications to the design of telecommunication
networks: the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP) where
a xed number of hubs have unlimited capacity, each non-hub node is allocated to a single hub
and the number of hubs is known in advance is analyzed in deterministic and stochastic scenarios.
p-hub median problems are concerned with optimality of telecommunication and transshipment
networks, and seek to minimize the cost of transportation or establishing.
Next, two immune inspired metaheuristics are proposed to solve the USApHMP, besides that,
a two-stage metaheuristic which relies on the combination of biased-randomized techniques with
an iterated local search framework and its integration with simulation Monte Carlo technique for
solving the same problem is proposed. In order to show their e ciency, a series of computational
tests are carried out using small and large size instances from the literature. These results contribute
to a deeper understanding of the e ectiveness of the employed metaheuristics for solving
the USApHMP in small and large networks. Finally, an illustrative application of the USApHMP
is presented as well as some insights about some new possibilities for it, extending the proposed
methodology to real-life environments.Els últims avenços en la industria de les telecomunicacions ofereixen grans oportunitats per
ciutadans i organitzacions en un món globalment connectat, però a la vegada, presenten reptes als
que s'enfronten tècnics i enginyers que prenen decisions. Alguns d'aquests reptes es poden modelitzar
com problemes d'optimització. Exemples inclouen l'assignació de recursos a les xarxes de
comunicació, trobant una topologia de xarxa que satisfà certes propietats associades a requisits de
qualitat de servei, xarxes multicast superposades i altres funcions importants per a la comunicació
origen a destinació.
El primer objectiu d'aquest treball és proporcionar un revisió de la literatura sobre com s'han
utilitzat aquestes tècniques, tradicionalment, per tractar els problemes d'optimització associats a
sistemes de telecomunicació, detectant les principals tendències i desa aments. Particularment,
l'estudi es centra en els problemes de disseny de xarxes, enrutament i problemes d'assignació de
recursos. Degut a la naturalesa d'aquests problemes, aquest treball també analitza com es poden
combinar les tècniques metaheurístiques amb metodologies de simulació per ampliar les capacitats
de resoldre problemes d'optimització estocàstics.
A més, es tracta un popular problema d'optimització amb aplicacions pràctiques per xarxes de
telecomunicació, el problema de la p mediana no capacitat, analitzant-lo des d'escenaris deterministes
i estocàstics. Aquest problema consisteix en determinar el nombre d'instal lacions (medianes)
en una xarxa, minimitzant la suma de tots els costs o distàncies des d'un punt de demanda a la
instal lació més propera. En general, el problema de la p mediana està lligat amb l'optimització de
xarxes de telecomunicacions i de transport, i busquen minimitzar el cost de transport o establiment
de la xarxa.
Es proposa dos algoritmes immunològics i un algoritme metaheurístic de dues etapes basat en
la combinació de tècniques aleatòries amb simulacions Monte Carlo. L'e ciència de les algoritmes
es posa a prova mitjançant alguns dels test computacionals més utilitzats a la literatura, obtenint
uns resultats molt satisfactoris, ja que es capaç de resoldre casos petits i grans en qüestió de segons i amb un baix cost computacional. Finalment, es presenta una aplicació il lustrativa del problema
de la p mediana, així com algunes noves idees sobre aquest, que estenen la metodologia proposta
a problemes de la vida real
Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization
Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems.
The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness.
The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin.
Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita.
ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed
Managing facility disruption in hub-and-spoke networks: formulations and efficient solution methods
Hub disruption result in substantially higher transportation cost and customer dissatisfaction. In this study, first a mathematical model to design hub-and-spoke networks under hub failure is presented. For a fast and inexpensive recovery, the proposed model constructs networks in which every single demand point will have a backup hub to be served from in case of disruption. The problem is formulated as a mixed integer quadratic program in a way that could be linearized without significantly increasing the number of variables. To further ease the model’ computational burden, indicator constraints are employed in the linearized model. The resulting formulation produced optimal solutions for small and some medium size instances. To tackle large problems, three efficient particle swarm optimisation-based metaheuristics which incorporate efficient solution representation, short-term memory and special crossover operator are proposed. We present the results for two scenarios relating to high and low probabilities of hub failures and provide managerial insight. The computational results, using problem instances with various sizes taken from CAB and TR datasets, confirm the effectiveness and efficiency of the proposed problem formulation and our new solution techniques
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
Genetic algorithm-based optimization approach for an uncapacitated single allocation P-hub center problem with more realistic cost structure
Abstract A p-hub center network design problem consists of some nodes as hubs and allocation of non-hub nodes to them wherein the maximum travel times between any pair of nodes is minimized. The distinctive feature of this study is proposing a new mathematical formulation for modeling costs in a p-hub center problem. Here, instead of considering costs as a linear function of distance, for the first time, we formulate costs as a summation of different parts: fixed cost, health, safety and environment (HSE) cost, energy cost and personnel cost. Such integrated model results in a hard-tosolve nonlinear formulation. To validate the proposed model, a small scale problem instance of CAB dataset solved by LINGO software. We also prepared a Genetic Algorithm (GA) by MATLAB software to solve complete problems of CAB and AP datasets
Comparison of the formulations for a hub-and-spoke network design problem under congestion
In this paper, we study the hub location problem with a power-law congestion cost and propose an exact solution approach. We formulate this problem in a conic quadratic form and use a strengthening method which rests on valid inequalities of perspective cuts in mixed integer nonlinear programming. In a numerical study, we compare two well known types of mathematical modeling in the hub-location problems which are solved with different branch and cut strategies. The strength and weakness of the formulations are summarized based on an extensive numerical study over the CAB data set
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