A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment

Transportation electrification has demonstrated a significant position on power utilities and logistic companies, in terms of assets operation and management. Under this context, this paper presents the problem of seeking feasible and good quality routes for electric light commercial vehicles consid...

Full description

Autores:
Arias-Londoño, Andrés
Gil-González, Walter
Montoya, Oscar Danilo
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/10329
Acceso en línea:
https://hdl.handle.net/20.500.12585/10329
Palabra clave:
Charging station
Electric vehicle
Energy losses
Logistics
Mixed integer programming model
Power distribution system
Routing
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc/4.0/
id UTB2_90df4cfc00da8fca5df25eea906f39d8
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/10329
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment
title A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment
spellingShingle A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment
Charging station
Electric vehicle
Energy losses
Logistics
Mixed integer programming model
Power distribution system
Routing
LEMB
title_short A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment
title_full A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment
title_fullStr A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment
title_full_unstemmed A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment
title_sort A linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessment
dc.creator.fl_str_mv Arias-Londoño, Andrés
Gil-González, Walter
Montoya, Oscar Danilo
dc.contributor.author.none.fl_str_mv Arias-Londoño, Andrés
Gil-González, Walter
Montoya, Oscar Danilo
dc.subject.keywords.spa.fl_str_mv Charging station
Electric vehicle
Energy losses
Logistics
Mixed integer programming model
Power distribution system
Routing
topic Charging station
Electric vehicle
Energy losses
Logistics
Mixed integer programming model
Power distribution system
Routing
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Transportation electrification has demonstrated a significant position on power utilities and logistic companies, in terms of assets operation and management. Under this context, this paper presents the problem of seeking feasible and good quality routes for electric light commercial vehicles considering battery capacity and charging station siting on the power distribution system. Different transportation patterns for goods delivery are included, such as the capacitated vehicle routing problem and the shortest path problem for the last mile delivery. To solve the problem framed within a mixed integer linear mathematical model, the GAMS software is used and validated on a test instance conformed by a 19-customer transportation network, spatially combined with the IEEE 34 nodes power distribution system. The sensitivity analysis, performed during the computational experiments, show the behavior of the variables involved in the logistics operation, i.e., routing cost for each transport pattern. The trade-off between the battery capacity, the cost of the charging station installation, and energy losses on the power distribution system is also shown, including the energy consumption cost created by the charging operation.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-07-29T18:26:48Z
dc.date.available.none.fl_str_mv 2021-07-29T18:26:48Z
dc.date.issued.none.fl_str_mv 2021-05-21
dc.date.submitted.none.fl_str_mv 2021-07-28
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.hasVersion.spa.fl_str_mv info:eu-repo/semantics/restrictedAccess
dc.type.spa.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.citation.spa.fl_str_mv Arias-Londoño, A.; Gil-González, W.; Montoya, O.D. A Linearized Approach for the Electric Light Commercial Vehicle Routing Problem Combined with Charging Station Siting and Power Distribution Network Assessment. Appl. Sci. 2021, 11, 4870. https://doi.org/10.3390/app11114870
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/10329
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Arias-Londoño, A.; Gil-González, W.; Montoya, O.D. A Linearized Approach for the Electric Light Commercial Vehicle Routing Problem Combined with Charging Station Siting and Power Distribution Network Assessment. Appl. Sci. 2021, 11, 4870. https://doi.org/10.3390/app11114870
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/10329
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessRights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Atribución-NoComercial 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Atribución-NoComercial 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 25 páginas
dc.format.medium.none.fl_str_mv PDF
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.publisher.sede.spa.fl_str_mv Campus Tecnológico
dc.source.spa.fl_str_mv Applied Sciences
institution Universidad Tecnológica de Bolívar
bitstream.url.fl_str_mv https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/1/applsci-11-04870.pdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/2/license_rdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/3/license.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/4/applsci-11-04870.pdf.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/5/applsci-11-04870.pdf.jpg
bitstream.checksum.fl_str_mv 864c8396019e6277e41b4854300d51ae
24013099e9e6abb1575dc6ce0855efd5
e20ad307a1c5f3f25af9304a7a7c86b6
6e2edd6227fb208184547da3490ffddd
3418f6080f41c7850a972ee58f3f2939
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional UTB
repository.mail.fl_str_mv repositorioutb@utb.edu.co
_version_ 1808397610509139968
spelling Arias-Londoño, Andrés89909de0-da09-49a3-8e61-83197925ba34Gil-González, Walterce1f5078-74c6-4b5c-b56a-784f85e52a08Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d44802021-07-29T18:26:48Z2021-07-29T18:26:48Z2021-05-212021-07-28Arias-Londoño, A.; Gil-González, W.; Montoya, O.D. A Linearized Approach for the Electric Light Commercial Vehicle Routing Problem Combined with Charging Station Siting and Power Distribution Network Assessment. Appl. Sci. 2021, 11, 4870. https://doi.org/10.3390/app11114870https://hdl.handle.net/20.500.12585/10329Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarTransportation electrification has demonstrated a significant position on power utilities and logistic companies, in terms of assets operation and management. Under this context, this paper presents the problem of seeking feasible and good quality routes for electric light commercial vehicles considering battery capacity and charging station siting on the power distribution system. Different transportation patterns for goods delivery are included, such as the capacitated vehicle routing problem and the shortest path problem for the last mile delivery. To solve the problem framed within a mixed integer linear mathematical model, the GAMS software is used and validated on a test instance conformed by a 19-customer transportation network, spatially combined with the IEEE 34 nodes power distribution system. The sensitivity analysis, performed during the computational experiments, show the behavior of the variables involved in the logistics operation, i.e., routing cost for each transport pattern. The trade-off between the battery capacity, the cost of the charging station installation, and energy losses on the power distribution system is also shown, including the energy consumption cost created by the charging operation.Universidad Tecnológica de Bolívar25 páginasPDFapplication/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Applied SciencesA linearized approach for the electric light commercial vehicle routing problem combined with charging station siting and power distribution network assessmentinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Charging stationElectric vehicleEnergy lossesLogisticsMixed integer programming modelPower distribution systemRoutingLEMBCartagena de IndiasCampus TecnológicoPúblico generalGlobal Energy Review 2019; Technical Report; International Energy Agency: Paris, France, 2019.World Energy Outlook, International Energy Agency; IEA: Paris, France, 2019.Tsakalidis, A.; Krause, J.; Julea, A.; Peduzzi, E.; Pisoni, E.; Thiel, C. Electric light commercial vehicles: Are they the sleeping giant of electromobility? Transp. Res. Part D Transp. Environ. 2020, 86, 102421.Christensen, L.; Klauenberg, J.; Kveiborg, O.; Rudolph, C. Suitability of commercial transport for a shift to electric mobility with Denmark and Germany as use cases. Res. Transp. Econ. 2017, 64, 48–60.Raeesi, R.; Zografos, K.G. The electric vehicle routing problem with time windows and synchronised mobile battery swapping. Transp. Res. Part B Methodol. 2020, 140, 101–129.Zhang, S.; Gajpal, Y.; Appadoo, S.; Abdulkader, M. Electric vehicle routing problem with recharging stations for minimizing energy consumption. Int. J. Prod. Econ. 2018, 203, 404–413.Alame, D.; Azzouz, M.; Kar, N. Assessing and Mitigating Impacts of Electric Vehicle Harmonic Currents on Distribution Systems. Energies 2020, 13, 3257.Londoño, A.; Granada-Echeverri, M. Optimal placement of freight electric vehicles charging stations and their impact on the power distribution network. Int. J. Ind. Eng. Comput. 2019, 10, 535–556.Arias, A.; Sanchez, J.; Granada, M. Integrated planning of electric vehicles routing and charging stations location considering transportation networks and power distribution systems. Int. J. Ind. Eng. Comput. 2018, 9, 535–550.Erdoğan, S.; Miller-Hooks, E. A green vehicle routing problem. Transp. Res. Part E: Logist. Transp. Rev. 2012, 48, 100–114.Afroditi, A.; Boile, M.; Theofanis, S.; Sdoukopoulos, E.; Margaritis, D. Electric vehicle routing problem with industry constraints: Trends and insights for future research. Transp. Res. Procedia 2014, 3, 452–459.Montoya, A.; Guéret, C.; Mendoza, J.E.; Villegas, J.G. A multi-space sampling heuristic for the green vehicle routing problem. Transp. Res. Part C Emerg. Technol. 2016, 70, 113–128.Felipe, Á.; Ortuño, M.T.; Righini, G.; Tirado, G. A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transp. Res. Part E Logist. Transp. Rev. 2014, 71, 111–128.Murakami, K.; Morita, H. A Column Generation Model for the Electric and Fuel-Engined Vehicle Routing Problem. In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China, 9–12 October 2015; pp. 1986–1991.Bruglieri, M.; Pezzella, F.; Pisacane, O.; Suraci, S. A variable neighborhood search branching for the electric vehicle routing problem with time windows. Electron. Notes Discret. Math. 2015, 47, 221–228.Keskin, M.; Çatay, B. Partial recharge strategies for the electric vehicle routing problem with time windows. Transp. Res. Part C Emerg. Technol. 2016, 65, 111–127. [Wu, Y.; Yang, M.; Zhang, H.; Chen, K.; Wang, Y. Optimal site selection of electric vehicle charging stations based on a cloud model and the PROMETHEE method. Energies 2016, 9, 157.Zhenfeng, G.; Yang, L.; Xiaodan, J.; Sheng, G. The electric vehicle routing problem with time windows using genetic algorithm. In Proceedings of the 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 25–26 March 2017; pp. 635–639.Zuo, X.; Zhu, C.; Huang, C.; Xiao, Y. Using AMPL/CPLEX to model and solve the electric vehicle routing problem (EVRP) with heterogeneous mixed fleet. In Proceedings of the 2017 29th Chinese Control In addition, Decision Conference (CCDC), Chongqing, China, 28–30 May 2017; pp. 4666–4670.Montoya, A.; Guéret, C.; Mendoza, J.E.; Villegas, J.G. The electric vehicle routing problem with nonlinear charging function. Transp. Res. Part B Methodol. 2017, 103, 87–110.De Cauwer, C.; Verbeke, W.; Coosemans, T.; Faid, S.; Van Mierlo, J. A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions. Energies 2017, 10, 608.Wu, Y.; Xie, C.; Xu, C.; Li, F. A decision framework for electric vehicle charging station site selection for residential communities under an intuitionistic fuzzy environment: A case of beijing. Energies 2017, 10, 1270.Mavrovouniotis, M.; Ellinas, G.; Polycarpou, M. Ant colony optimization for the electric vehicle routing problem. In Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 18–21 November 2018; pp. 1234–1241.Yi, Z.; Bauer, P.H. Optimal stochastic eco-routing solutions for electric vehicles. IEEE Trans. Intell. Transp. Syst. 2018, 19, 3807–3817.Deb, S.; Tammi, K.; Kalita, K.; Mahanta, P. Review of recent trends in charging infrastructure planning for electric vehicles. Wiley Interdiscip. Rev. Energy Environ. 2018, 7, e306.Mouhrim, N.; Alaoui, A.E.H.; Boukachour, J. Vehicle routing problem with mixed fleet of electric and conventional vehicles under emissions allowances. In Proceedings of the 2018 4th International Conference on Logistics Operations Management (GOL), Le Havre, France, 10–12 April 2018; pp. 1–5.Keskin, M.; Çatay, B. A matheuristic method for the electric vehicle routing problem with time windows and fast chargers. Comput. Oper. Res. 2018, 100, 172–188.Zhou, B.H.; Tan, F. Electric vehicle handling routing and battery swap station location optimisation for automotive assembly lines. Int. J. Comput. Integr. Manuf. 2018, 31, 978–991.Lu, J.; Wang, L. A Bi-Strategy Based Optimization Algorithm for the Dynamic Capacitated Electric Vehicle Routing Problem. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, 10–13 June 2019; pp. 646–653.Zhen, L.; Xu, Z.; Ma, C.; Xiao, L. Hybrid electric vehicle routing problem with mode selection. Int. J. Prod. Res. 2020, 58, 562–576.Jie, W.; Yang, J.; Zhang, M.; Huang, Y. The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology. Eur. J. Oper. Res. 2019, 272, 879–904.Basso, R.; Kulcsár, B.; Egardt, B.; Lindroth, P.; Sanchez-Diaz, I. Energy consumption estimation integrated into the electric vehicle routing problem. Transp. Res. Part D Transp. Environ. 2019, 69, 141–167.Pelletier, S.; Jabali, O.; Laporte, G. The electric vehicle routing problem with energy consumption uncertainty. Transp. Res. Part B Methodol. 2019, 126, 225–255.Zuo, X.; Xiao, Y.; You, M.; Kaku, I.; Xu, Y. A new formulation of the electric vehicle routing problem with time windows considering concave nonlinear charging function. J. Clean. Prod. 2019, 236, 117687.Froger, A.; Mendoza, J.E.; Jabali, O.; Laporte, G. Improved formulations and algorithmic components for the electric vehicle routing problem with nonlinear charging functions. Comput. Oper. Res. 2019, 104, 256–294.Yi, T.; Cheng, X.B.; Zheng, H.; Liu, J.P. Research on location and capacity optimization method for electric vehicle charging stations considering user’s comprehensive satisfaction. Energies 2019, 12, 1915.Jiang, X.; Guo, X. Evaluation of performance and technological characteristics of battery electric logistics vehicles: China as a case study. Energies 2020, 13, 2455.Gerber Machado, P.; Rodrigues Teixeira, A.C.; Mendes de Almeida Collaço, F.; Hawkes, A.; Mouette, D. Assessment of Greenhouse Gases and Pollutant Emissions in the Road Freight Transport Sector: A Case Study for São Paulo State, Brazil. Energies 2020, 13, 5433.Mao, H.; Shi, J.; Zhou, Y.; Zhang, G. The Electric Vehicle Routing Problem with Time Windows and Multiple Recharging Options. IEEE Access 2020, 8, 114864–114875. [Kancharla, S.R.; Ramadurai, G. Electric vehicle routing problem with nonlinear charging and load-dependent discharging. Expert Syst. Appl. 2020, 160, 113714.Zhang, S.; Chen, M.; Zhang, W.; Zhuang, X. Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations. Expert Syst. Appl. 2020, 145, 113123.Almouhanna, A.; Quintero-Araujo, C.L.; Panadero, J.; Juan, A.A.; Khosravi, B.; Ouelhadj, D. The location routing problem using electric vehicles with constrained distance. Comput. Oper. Res. 2020, 115, 104864.Zhu, X.; Yan, R.; Huang, Z.; Wei, W.; Yang, J.; Kudratova, S. Logistic optimization for multi depots loading capacitated electric vehicle routing problem from low carbon perspective. IEEE Access 2020, 8, 31934–31947.Bahrami, S.; Nourinejad, M.; Amirjamshidi, G.; Roorda, M.J. The Plugin Hybrid Electric Vehicle routing problem: A power-management strategy model. Transp. Res. Part C Emerg. Technol. 2020, 111, 318–333.Baek, D.; Chen, Y.; Chang, N.; Macii, E.; Poncino, M. Battery-Aware Electric Truck Delivery Route Exploration. Energies 2020, 13, 2096.Toth, P.; Vigo, D. The Vehicle Routing Problem; SIAM: Bologna, Italy, 2002.Yang, J.; Sun, H. Battery swap station location-routing problem with capacitated electric vehicles. Comput. Oper. Res. 2015, 55, 217–232.Halldórsson, Á.; Wehner, J. Last-mile logistics fulfilment: A framework for energy efficiency. Res. Transp. Bus. Manag. 2020, 37, 100481.Juan, A.A.; Mendez, C.A.; Faulin, J.; De Armas, J.; Grasman, S.E. Electric vehicles in logistics and transportation: A survey on emerging environmental, strategic, and operational challenges. Energies 2016, 9, 86.de Andrade, R.C.; Saraiva, R.D. An integer linear programming model for the constrained shortest path tour problem. Electron. Notes Discret. Math. 2018, 69, 141–148.Garces, A. A linear three-phase load flow for power distribution systems. IEEE Trans. Power Syst. 2015, 31, 827–828.Stevenson, W.; Grainger, J. Power System Analysis; McGraw-Hill Education: Raleigh, NC, USA, 1994.The Ybus Admittance Matrix for Solving Power Flow Equations. YouTube Video. 2016. Available online: https:www.youtube.com/watch?v=8jNuSwnL7HE (accessed on 15 January 2021).Qian, K.; Zhou, C.; Allan, M.; Yuan, Y. Effect of load models on assessment of energy losses in distributed generation planning. Int. J. Electr. Power Energy Syst. 2011, 33, 1243–1250.Flanigan, F.J. Complex Variables: Harmonic and Analytic Functions; Courier Corporation: North Chelmsford, MA, USA, 1983.Garces, A. A quadratic approximation for the optimal power flow in power distribution systems. Electr. Power Syst. Res. 2016, 130, 222–229.IEEE PES AMPS DSAS Test Feeder Working Group. 34-bus Feeder. Available online: https://site.ieee.org/pes-testfeeders/resources/ (accessed on 15 October 2020).Networking and Emerging Optimization. Capacitated VRP Instances. Available online: https://neo.lcc.uma.es/vrp/vrp-instances/capacitated-vrp-instances/ (accessed on 15 October 2020).Andres, A.L. Test System for the eLCVRP-CS-PDS. 2021. Available online: https://academia.utp.edu.co/planeamiento/files/2021/02/Pn19k2-IEEE34-elCVRP-CS-PDS.xlsx (accessed on 15 November 2020).Quebec, H. Electric Vehicle Charging Stations Technical Installation Guide. Hydro Quebéc 2015. Available online: https://www.hydroquebec.com/data/electrification-transport/pdf/technical-guide.pdf (accessed on 30 October 2020).Nicholas, M. Estimating Electric Vehicle Charging Infrastructure Costs across Major US Metropolitan Areas. 2019. Available online: https://theicct.org/publications/charging-cost-US (accessed on 30 October 2020).http://purl.org/coar/resource_type/c_6501ORIGINALapplsci-11-04870.pdfapplsci-11-04870.pdfArtículoapplication/pdf2709299https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/1/applsci-11-04870.pdf864c8396019e6277e41b4854300d51aeMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/2/license_rdf24013099e9e6abb1575dc6ce0855efd5MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXTapplsci-11-04870.pdf.txtapplsci-11-04870.pdf.txtExtracted texttext/plain80753https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/4/applsci-11-04870.pdf.txt6e2edd6227fb208184547da3490ffdddMD54THUMBNAILapplsci-11-04870.pdf.jpgapplsci-11-04870.pdf.jpgGenerated Thumbnailimage/jpeg104531https://repositorio.utb.edu.co/bitstream/20.500.12585/10329/5/applsci-11-04870.pdf.jpg3418f6080f41c7850a972ee58f3f2939MD5520.500.12585/10329oai:repositorio.utb.edu.co:20.500.12585/103292021-07-30 01:59:58.825Repositorio Institucional UTBrepositorioutb@utb.edu.coQXV0b3Jpem8gKGF1dG9yaXphbW9zKSBhIGxhIEJpYmxpb3RlY2EgZGUgbGEgSW5zdGl0dWNpw7NuIHBhcmEgcXVlIGluY2x1eWEgdW5hIGNvcGlhLCBpbmRleGUgeSBkaXZ1bGd1ZSBlbiBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsLCBsYSBvYnJhIG1lbmNpb25hZGEgY29uIGVsIGZpbiBkZSBmYWNpbGl0YXIgbG9zIHByb2Nlc29zIGRlIHZpc2liaWxpZGFkIGUgaW1wYWN0byBkZSBsYSBtaXNtYSwgY29uZm9ybWUgYSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBxdWUgbWUobm9zKSBjb3JyZXNwb25kZShuKSB5IHF1ZSBpbmNsdXllbjogbGEgcmVwcm9kdWNjacOzbiwgY29tdW5pY2FjacOzbiBww7pibGljYSwgZGlzdHJpYnVjacOzbiBhbCBww7pibGljbywgdHJhbnNmb3JtYWNpw7NuLCBkZSBjb25mb3JtaWRhZCBjb24gbGEgbm9ybWF0aXZpZGFkIHZpZ2VudGUgc29icmUgZGVyZWNob3MgZGUgYXV0b3IgeSBkZXJlY2hvcyBjb25leG9zIHJlZmVyaWRvcyBlbiBhcnQuIDIsIDEyLCAzMCAobW9kaWZpY2FkbyBwb3IgZWwgYXJ0IDUgZGUgbGEgbGV5IDE1MjAvMjAxMiksIHkgNzIgZGUgbGEgbGV5IDIzIGRlIGRlIDE5ODIsIExleSA0NCBkZSAxOTkzLCBhcnQuIDQgeSAxMSBEZWNpc2nDs24gQW5kaW5hIDM1MSBkZSAxOTkzIGFydC4gMTEsIERlY3JldG8gNDYwIGRlIDE5OTUsIENpcmN1bGFyIE5vIDA2LzIwMDIgZGUgbGEgRGlyZWNjacOzbiBOYWNpb25hbCBkZSBEZXJlY2hvcyBkZSBhdXRvciwgYXJ0LiAxNSBMZXkgMTUyMCBkZSAyMDEyLCBsYSBMZXkgMTkxNSBkZSAyMDE4IHkgZGVtw6FzIG5vcm1hcyBzb2JyZSBsYSBtYXRlcmlhLgoKQWwgcmVzcGVjdG8gY29tbyBBdXRvcihlcykgbWFuaWZlc3RhbW9zIGNvbm9jZXIgcXVlOgoKLSBMYSBhdXRvcml6YWNpw7NuIGVzIGRlIGNhcsOhY3RlciBubyBleGNsdXNpdmEgeSBsaW1pdGFkYSwgZXN0byBpbXBsaWNhIHF1ZSBsYSBsaWNlbmNpYSB0aWVuZSB1bmEgdmlnZW5jaWEsIHF1ZSBubyBlcyBwZXJwZXR1YSB5IHF1ZSBlbCBhdXRvciBwdWVkZSBwdWJsaWNhciBvIGRpZnVuZGlyIHN1IG9icmEgZW4gY3VhbHF1aWVyIG90cm8gbWVkaW8sIGFzw60gY29tbyBsbGV2YXIgYSBjYWJvIGN1YWxxdWllciB0aXBvIGRlIGFjY2nDs24gc29icmUgZWwgZG9jdW1lbnRvLgoKLSBMYSBhdXRvcml6YWNpw7NuIHRlbmRyw6EgdW5hIHZpZ2VuY2lhIGRlIGNpbmNvIGHDsW9zIGEgcGFydGlyIGRlbCBtb21lbnRvIGRlIGxhIGluY2x1c2nDs24gZGUgbGEgb2JyYSBlbiBlbCByZXBvc2l0b3JpbywgcHJvcnJvZ2FibGUgaW5kZWZpbmlkYW1lbnRlIHBvciBlbCB0aWVtcG8gZGUgZHVyYWNpw7NuIGRlIGxvcyBkZXJlY2hvcyBwYXRyaW1vbmlhbGVzIGRlbCBhdXRvciB5IHBvZHLDoSBkYXJzZSBwb3IgdGVybWluYWRhIHVuYSB2ZXogZWwgYXV0b3IgbG8gbWFuaWZpZXN0ZSBwb3IgZXNjcml0byBhIGxhIGluc3RpdHVjacOzbiwgY29uIGxhIHNhbHZlZGFkIGRlIHF1ZSBsYSBvYnJhIGVzIGRpZnVuZGlkYSBnbG9iYWxtZW50ZSB5IGNvc2VjaGFkYSBwb3IgZGlmZXJlbnRlcyBidXNjYWRvcmVzIHkvbyByZXBvc2l0b3Jpb3MgZW4gSW50ZXJuZXQgbG8gcXVlIG5vIGdhcmFudGl6YSBxdWUgbGEgb2JyYSBwdWVkYSBzZXIgcmV0aXJhZGEgZGUgbWFuZXJhIGlubWVkaWF0YSBkZSBvdHJvcyBzaXN0ZW1hcyBkZSBpbmZvcm1hY2nDs24gZW4gbG9zIHF1ZSBzZSBoYXlhIGluZGV4YWRvLCBkaWZlcmVudGVzIGFsIHJlcG9zaXRvcmlvIGluc3RpdHVjaW9uYWwgZGUgbGEgSW5zdGl0dWNpw7NuLCBkZSBtYW5lcmEgcXVlIGVsIGF1dG9yKHJlcykgdGVuZHLDoW4gcXVlIHNvbGljaXRhciBsYSByZXRpcmFkYSBkZSBzdSBvYnJhIGRpcmVjdGFtZW50ZSBhIG90cm9zIHNpc3RlbWFzIGRlIGluZm9ybWFjacOzbiBkaXN0aW50b3MgYWwgZGUgbGEgSW5zdGl0dWNpw7NuIHNpIGRlc2VhIHF1ZSBzdSBvYnJhIHNlYSByZXRpcmFkYSBkZSBpbm1lZGlhdG8uCgotIExhIGF1dG9yaXphY2nDs24gZGUgcHVibGljYWNpw7NuIGNvbXByZW5kZSBlbCBmb3JtYXRvIG9yaWdpbmFsIGRlIGxhIG9icmEgeSB0b2RvcyBsb3MgZGVtw6FzIHF1ZSBzZSByZXF1aWVyYSBwYXJhIHN1IHB1YmxpY2FjacOzbiBlbiBlbCByZXBvc2l0b3Jpby4gSWd1YWxtZW50ZSwgbGEgYXV0b3JpemFjacOzbiBwZXJtaXRlIGEgbGEgaW5zdGl0dWNpw7NuIGVsIGNhbWJpbyBkZSBzb3BvcnRlIGRlIGxhIG9icmEgY29uIGZpbmVzIGRlIHByZXNlcnZhY2nDs24gKGltcHJlc28sIGVsZWN0csOzbmljbywgZGlnaXRhbCwgSW50ZXJuZXQsIGludHJhbmV0LCBvIGN1YWxxdWllciBvdHJvIGZvcm1hdG8gY29ub2NpZG8gbyBwb3IgY29ub2NlcikuCgotIExhIGF1dG9yaXphY2nDs24gZXMgZ3JhdHVpdGEgeSBzZSByZW51bmNpYSBhIHJlY2liaXIgY3VhbHF1aWVyIHJlbXVuZXJhY2nDs24gcG9yIGxvcyB1c29zIGRlIGxhIG9icmEsIGRlIGFjdWVyZG8gY29uIGxhIGxpY2VuY2lhIGVzdGFibGVjaWRhIGVuIGVzdGEgYXV0b3JpemFjacOzbi4KCi0gQWwgZmlybWFyIGVzdGEgYXV0b3JpemFjacOzbiwgc2UgbWFuaWZpZXN0YSBxdWUgbGEgb2JyYSBlcyBvcmlnaW5hbCB5IG5vIGV4aXN0ZSBlbiBlbGxhIG5pbmd1bmEgdmlvbGFjacOzbiBhIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBkZSB0ZXJjZXJvcy4gRW4gY2FzbyBkZSBxdWUgZWwgdHJhYmFqbyBoYXlhIHNpZG8gZmluYW5jaWFkbyBwb3IgdGVyY2Vyb3MgZWwgbyBsb3MgYXV0b3JlcyBhc3VtZW4gbGEgcmVzcG9uc2FiaWxpZGFkIGRlbCBjdW1wbGltaWVudG8gZGUgbG9zIGFjdWVyZG9zIGVzdGFibGVjaWRvcyBzb2JyZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBsYSBvYnJhIGNvbiBkaWNobyB0ZXJjZXJvLgoKLSBGcmVudGUgYSBjdWFscXVpZXIgcmVjbGFtYWNpw7NuIHBvciB0ZXJjZXJvcywgZWwgbyBsb3MgYXV0b3JlcyBzZXLDoW4gcmVzcG9uc2FibGVzLCBlbiBuaW5nw7puIGNhc28gbGEgcmVzcG9uc2FiaWxpZGFkIHNlcsOhIGFzdW1pZGEgcG9yIGxhIGluc3RpdHVjacOzbi4KCi0gQ29uIGxhIGF1dG9yaXphY2nDs24sIGxhIGluc3RpdHVjacOzbiBwdWVkZSBkaWZ1bmRpciBsYSBvYnJhIGVuIMOtbmRpY2VzLCBidXNjYWRvcmVzIHkgb3Ryb3Mgc2lzdGVtYXMgZGUgaW5mb3JtYWNpw7NuIHF1ZSBmYXZvcmV6Y2FuIHN1IHZpc2liaWxpZGFkCgo=