Top PDF Multiple Charging Station Location-Routing Problem with Time Window of Electric Vehicle

Multiple Charging Station Location-Routing Problem with Time Window of Electric Vehicle

Multiple Charging Station Location-Routing Problem with Time Window of Electric Vehicle

The second strand of the relevant literature consists of the routing problems that consider the limited driving range of vehicles and the possibility of refueling en route. Conrad and Figliozzi introduced the recharging VRP, in which vehicles with a limited range are allowed to recharge at certain customer stations within a fixed time [23]. Erdogan and Miller-Hookers presented the green VRP (G-VRP) for routing AFVs and solved the problem with two algorithms. In G-VRP, refueling stations are assumed to be independent of customer sites, and an AFV may refuel at these stations within a fixed time [24]. Later, Schneider et al. incorporated the time window constraint into the G-VRP and proposed the EV-routing problem with time windows and recharging station (E-VRPTW) [7]. The charging time in E-VRPTW is not fixed but instead is related to the battery charge of an EV upon arrival at the station. To address the problem, they developed a hybrid heuristic that combines the VNS with the TS algorithm (VNS/TS). Schneider et al. then introduced the VRP with intermediate stops (VRPIS), which generalized the G-VRP, and solved the problem by AVNS [29]. Five route selection methods and three vertex sequence selection methods were utilized in the adaptive shaking phase of AVNS. Felipe et al. proposed several heuristics to address the G-VRP with multiple technologies and partial recharges. The problem extends the G-VRP by incorporating different technologies for battery recharge and the possibility of partial recharges [25]. Goeke and Schneider combined the E- VRPTW with a mixed fleet of EVs and ICVs, and utilized realistic energy consumption functions in their problem [26]. The resulting problem was solved by an ALNS with a local search for intensification. Yang and Sun adopted the simultaneous optimization idea from the LRP to the context of EV and proposed the BSS location-routing problem of EVs [6]. The problem is intended to minimize infrastructure and shipping costs by determining the station location and vehicle-routing plan jointly under a driving range limitation. For the solution method, they employed the concept of solving separate sub-problems iteratively from the LRP and proposed two hybrid heuristics [22]. In detail, one algorithm called TS-modified Clarke–Wright saving (MCWS) combines the TS algorithm for location strategy and the MCWS method for the routing decision. The other approach named SIGALNS includes four main phases: initialization, location sub-problem, routing sub-problem, and improvement. Iterative greedy (IG) is utilized in the location phase, and an ALNS in the routing phase.
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Assignment Problem and Vehicle Routing Problem for an Improvement of Cash Distribution

Assignment Problem and Vehicle Routing Problem for an Improvement of Cash Distribution

delivery cash to and from each branch everyday in a given different time window. The most suitable methodology for this problem would be multiple depot vehicle routing problems with time window. However, the answer of this research is new routes which can handle changes in daily operations, i.e., change in demands, change in operation time, etc., therefore, N&C needs a methodology which can give results in a short processing time. The researcher decides to use easy and quick algorithm to solve this problem. There are two main methodologies used in this research. First, assignment problem is employed and capacitated vehicle routing problem with time window (VRPTW) is used later. The assignment problem clusters 377 branches into 3 groups each group belongs to each DC and VRPTW produces routes for each DC daily.
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Electric vehicle charging process and parking guidance app

Electric vehicle charging process and parking guidance app

The first option of the application “Navigate”, opening a new window with two input boxes that the user must fill. The first input being the destination where the user wants to travel to and the second the current battery level the EV has (in an ideal scenario, this information could be accessed trough the vehicle API). After filling those inputs, the user can confirm and a route to the closest and more likely to have an available place to charge is selected in the surroundings of the searched destination, by a decision algorithm created in this research. After that, a route is created using the Google Directions API, starting at the current location of the user to the respective CS. The CS is chosen by the decision algorithm, that takes into account various conditions: the first and most important is the maximum possible distance the EV can travel, meaning that all CS further than the maximum possible distance can not be taken into account, so they are automatically discarded and a pop-up window is sent to the user informing that the desired destination cannot be reached with the current vehicle battery. The second most important factor to take into account is the probability to have a free charging spot on each CS taking into consideration the time it takes the user to reach the CS. This can be achieved by the predictive model also being developed in this study. The route generated, just like in [2], provides information like the battery level the EV has at the end of the route, considering regenerative braking, if possible, the occupancy of the CS at arrival and the time it would take to reach the CS. The time it would take to reach the CS makes it possible to feed the predictive model with the time of arrival and is also used as the third condition, already taking into account the intensity of traffic, where the CS with less travel time has a higher possibility to be chosen. The last heuristic used is the distance to the CS, where the closest station to the destination the user provided has a bigger probability to be chosen.
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Preliminary research about electric vehicle charging stations / Pesquisa preliminar sobre estações de carregamento de veículos elétricos

Preliminary research about electric vehicle charging stations / Pesquisa preliminar sobre estações de carregamento de veículos elétricos

The size of the load required to charge plug-in electric vehicles can cause a low voltage problem in the distribution network. In order to mitigate this problem, it is necessary to limit the power consumption of the charging station (KIM et al. 2016). Thus, the station operator needs a method to properly distribute the energy to the vehicles, with that in mind, Kim et al. (2016) propose a method based on priority indices to properly distribute energy among electric vehicles. Electric vehicles are ranked by priority in real time using computer software, achieving an almost optimal solution in a short period of time. As a result, the simulations performed by the author prove the effectiveness of the method implementation.
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Solving Public Transit Scheduling Problems

Solving Public Transit Scheduling Problems

In section 2 a review of relevant research on the integrated vehicle and crew scheduling process, as well as on the rostering issue is presented followed by a brief description of the integrated vehicle-crew-rostering problem itself (VCRP, for short). Section 3 proposes a multi-objective model for the VCRP, along with a mathematical formulation. Section 4 is devoted to the solution methodologies. The solution approach consists of applying rostering after the integrated vehicle and crew scheduling module following a preemptive goal programming approach. Some parameters controlled by the user during the optimization of the integrated vehicle and crew scheduling problem can be adjusted during the overall process in order to obtain better final solutions. In section 5, the results of a computational experiment with a set of problems taken from a real bus company are given. The conclusions, in section 6, point to the need to continue researching into solution methods to produce efficient software for a complete integration of the scheduling problems in a public transit company.
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Vehicle routing and tour planning problem: a cement industry case study

Vehicle routing and tour planning problem: a cement industry case study

It is important to remind that, as there are no updates in the routes, being all the calculations made at the time of each trucks’ check-in, the servers’ workload could change and the servers with less occupation now may or may not be in the same servers in the next units of time, due to the changing of the workload configuration. In a little example, assume that a truck X3 enters a plant in a specific time, and must be served on the servers A, B and C. Assume now that, considering all the trucks already inside the plant, the sequence of servers for X3 is B-C-A, being the routes connecting the servers the minimum time ones. Consider now that, after X3 enters, several trucks arrive at the plant. These trucks have only one required place, and it is the server C. The algorithm will give the minimum time routes for that trucks to reach C, because there are no other required places for them to be guided. Therefore, when X3 reaches C, after being served in B, the server C will be more occupied than what have been previously calculated. This may result in a delayed service for the truck X3. In this particular situation, it would be preferable, both for the system and for the truck X3, for X3 to go first to the server A, if the workload in A is less than C at the time of reaching C.
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Combining an evolutionary algorithm with data mining to solve a single-vehicle routing problem

Combining an evolutionary algorithm with data mining to solve a single-vehicle routing problem

are abundant in the literature, the application of DM to improve the results of evolutionary algorithms is still scarce. The DM module proposed corresponds to an intensification strategy, since it tries to discover good features in the best solutions found so far and to apply them in the generation of new solutions. The addition of the DM module into the GA significantly improved this method and the hybrid version with local search (GADMLS), on average, produced the better results. Results could be improved if other interactions between modules and/or a more exhaustive set of experiments were conducted (perhaps, larger running times would benefit the more computationally expensive version—GADMLS). Nonetheless, our proposal looks very promising, specially considering problems in which it is difficult to devise efficient local search algorithms.
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HLAODV - A Cross Layer Routing Protocol for Pervasive Heterogeneous Wireless Sensor Networks Based On Location

HLAODV - A Cross Layer Routing Protocol for Pervasive Heterogeneous Wireless Sensor Networks Based On Location

personalized services while ensuring a fair degree of privacy / non-intrusiveness. The goal of pervasive computing is to create ambient- intelligence, reliable connectivity, and secure and ubiquitous services in order to adapt to the associated context and activity. To make this envision a reality, various interconnected sensor networks have to be set up to collect context information, providing context-aware pervasive computing with adaptive capacity to dynamically changing environment. Wireless sensor networks (WSN) can help people to be aware of a lot of particular and reliable information anytime anywhere by monitoring, sensing, collecting and processing the information of various environments and scattered objects [24]. The flexibility, fault tolerance, high sensing, self- organization, fidelity, low-cost and rapid deployment characteristics of sensor networks are ideal to many new and exciting application areas such as military, environment monitoring, intelligent control, traffic management, medical treatment, manufacture industry, antiterrorism and so on [18,23]. Therefore, recent years have witnessed the rapid development of WSNs. In this paper, we address the issue of cross-layer networking for the pervasive networks , where the physical and MAC layer knowledge of the wireless medium is shared with network layer, in order to provide efficient routing scheme to prolong the network life time.
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Multi-Objective Forest Vehicle Routing Using Savings-Insertion and Reactive Tabu with a Variable Threshold

Multi-Objective Forest Vehicle Routing Using Savings-Insertion and Reactive Tabu with a Variable Threshold

resolution method used was a bi-objective tabu search algorithm. The first objective is to minimize the total number of vehicles used, and the second, to minimize the total cost, which is the weighted sum of the total distance traveled and the corresponding total time. Ref. [13], based on the work of [12], established a multi-criteria optimization model of long-haul VRP and scheduling integrating working hours rules. The solution method used was a bi- objective tabu search algorithm determining a set of heuristic non-dominated solutions. The mechanism consists of a single thread in which the weights assigned to the two objectives, namely, operating costs and driver inconvenience, are dynamically modified, and in which dominated solutions are eliminated throughout the search. Ref. [14] proposed a multi-depot VRP with a simultaneous delivery and pick-up model. The resolution method used was the iterated local search embedded adaptive neighborhood selection approach. Ref. [15] tested local search move operators on the VRP with split deliveries and time windows. To that end, they used eight local search opera-tors, in combinations of up to three of them, paired with a max-min ant system. Ref. [16] developed a dynamic model for solving the mixed integer programming of forest plant location and design, as well as production levels and flows between origins and destinations. Ref. [17] proposed a multi-depot forest transportation model solving the tactical problem of the flow between origins and destinations without solving the operational problem of VRP. The solution method used was column generation. Ref. [6] proposed a model for forest transportation, solving the problem of flow between origins and destinations, and involving a sedimentation constraint
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OPTIMIZATION MODEL FOR VEHICLE ROUTING AND EQUIPMENT REPLACEMENT IN FARM MACHINERY

OPTIMIZATION MODEL FOR VEHICLE ROUTING AND EQUIPMENT REPLACEMENT IN FARM MACHINERY

ABSTRACT: An equipment replacement decision takes into account economic engineering models based on discounted cash flow (DCF) such as the Annual Equivalent Cost (AEC). Despite a large number of researches on industrial assets replacement, there is a lack of studies applied to farm goods. This study aimed at assessing an alternative model for economic decision analysis on farm machinery replacement, with no restrictions on the number of replacements and assessed goods during a defined timeline. The results of the hybrid model based on the combination of the vehicle routing problem and the equipment replacement problem (RVPSE) applied to three different farm tractors showed the model reliability, providing a wider range of decisions for management support. KEYWORDS: economic engineering, annual equivalent cost, integer linear programming.
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Unified motor drive and battery charger for electric vehicles

Unified motor drive and battery charger for electric vehicles

The immunity of the proposed DPC-MPC to the variations of the input filter inductances’ values, comparatively to the values defined in the predictive model, is discussed in [109], but considering that both three inductances vary equally from the value defined in the model. The results obtained concluded that the proposed algorithm has proved to be robust when errors in the inductance values are present (establishing an inductance value of 10 mH and range their real value between 5 mH and 20 mH) [109]. However, only an analysis on the results obtained using this control algorithm in the unified topology proposed in this MSc. Thesis can assess the effective need of changes in the control algorithm. These may involve considering the inductances matrix and the possible range of the values of the individual inductances, according to the rotor position. Nevertheless, any constraint can generally be included in the predictive model and the cost function of the MPC, since it can be mathematically modeled and measured [104]. Thus, this control algorithm seems to offer greater ease in solving the possible disturbances that may occur, due to the use of the windings of the electrical machine, in a simplest way than using the DCC-SRF.
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Ant colony optimization techniques for the hamiltonian p-median problem

Ant colony optimization techniques for the hamiltonian p-median problem

In the early years, specialized heuristics were typically developed to solve complex CO problems (Glover coined in 1986 the term metaheuristics for such methods). Ant colony optimization (ACO) is one of the most recent metaheuristic techniques. The inspiring source of ACO algorithms are real ant colonies. More specifically, ACO is inspired by the ants’ foraging behavior. At the core of this behavior is the indirect communication between the ants by means of chemical pheromone trails, which enables them to find short paths between their nest and food sources. This characteristic of real ant colonies is exploited in ACO algorithms in order to solve, for example, CO problems.
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Planning of vehicle routing with backup provisioning using wireless sensor technologies

Planning of vehicle routing with backup provisioning using wireless sensor technologies

The iSense Security Module is used to capture data inside a vehicle while the Coalesenses Gateway Module is used to forward data to the monitoring tool through the collector node at the station, as shown in Figure 1. That is, a gateway exists per vehicle. The protocol stack implemented at the devices was the IPv6 over Low power Wireless Personal Area Networks (6LoWPAN), allowing for an easier integration of nodes into the Internet and similar management to standard IP-based networks. The gateway is configured to assign IPv6 addresses to all sensor/PIR nodes, and once the nodes receive an IPv6 address, they automatically store the IPv6 address of the gateway allowing them to send their data to the gateway. In order not to assign IP addresses to nodes of nearby vehicles, the IP assignment takes MAC addresses (of nodes inside the same vehicle as the gateway) into consideration. That is, the assignment of IP addresses, done by the gateway, should be restricted to the nodes inside the same vehicle. The area of action of the PIRs was reduced to a narrow beam, more specifically from the default 110 ◦ to 20–30 ◦ . This implementation was tested in a single vehicle on our campus, with success. The advantages of the backup provisioning approach need to be verified before installing the equipment in multiple vehicles.
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Algoritmos para o problema de roteamento de veículos capacitado com restrições de carregamento bidimensional

Algoritmos para o problema de roteamento de veículos capacitado com restrições de carregamento bidimensional

Além do CVRP, outras versões do VRP são muito estudadas. O Problema de Roteamento de Veículos com Janelas de Tempo (VRPTW, do inglês Vehicle Routing Problem with Time Windows) é uma extensão do CVRP onde cada cliente deve ter seu atendimento iniciado em uma janela de tempo e o veículo associado deve atendê-lo durante um tempo previamente estipulado. Por sua vez, o Problema de Roteamento de Veículos com Backhauls (VRPB, do inglês Vehicle Routing Problem with Backhauls) consiste em um CVRP onde o conjunto de clientes é particionado em dois subconjuntos: linehaul e backhaul. O primeiro subconjunto consiste nos clientes que necessitam de itens a serem entregues, enquanto o segundo representa os clientes que dispõem de itens a serem coletados. No VRPB, todos os clientes linehaul devem ser visitados antes dos clientes backhaul. Uma outra variação do VRP é o Problema de Roteamento de Veículos com Coleta e Entrega (VRPPD, do inglês Vehicle Routing Problem with Pick-ups and Deliveries), onde uma requisição de transporte é associada a dois clientes, de tal forma que a demanda é coletada em um deles e entregue no outro. Nesse problema, uma solução viável requer que a coleta de uma requisição seja feita antes de sua entrega, e que ambas operações ocorram na mesma rota. Informações sobre os trabalhos propostos e os detalhes do VRPTW, VRPB e VRPPD, podem ser encontrados em Alvarenga et al. [2007], Toth & Vigo [2001c] e Desaulniers et al. [2001], respectivamente.
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A multi agent based system to enable dynamic vehicle routing

A multi agent based system to enable dynamic vehicle routing

Abstract: The transport activities usually involves several actors and vehicles spread out on a network of streets. This complex system intricate the techniques to deal with dynamic events usually present in transport operations. In this context, as could be noted in the literature review, the use of multi-agent systems (MAS) seems suitable to support the autonomous decision-making. This work presents an agent based system to deal with a dynamic vehicle routing problem, more precisely, in a pick-up problem, where some tasks assigned to vehicles at the beginning of the operation could be transferred to others vehicles. The task transfer happens when the vehicle agents perceive that the cycle time can exceed the daily limit of working hours, and is done through a negotiation protocol called Vickrey. The proposed system allows a collaborative decision- making among the agents, which makes possible adjustments during the course of the planned route.
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Standalone docking station with combined charging methods for agricultural mobile robots

Standalone docking station with combined charging methods for agricultural mobile robots

The most common operating principal of this type of charger is based on magnetic coupling. We will have in our charger coils that, due to the passage of electric current in the coil wires, creates a magnetic field that in turn will induce a current in the coils of the battery, charging it [1], [2]. A loading based on inductive resonant coupling which allows to carry out the process of charging with a fix centimeters distance between the charger and the device is being developed. This would be a benefit since it would enable to have a protective structure over the charging base. This structure would be important to avoid short-circuit to happen when it rains or to avoid battery explosion when there are very high temperatures.
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A BROADCAST BASED ROUTING PROTOCOL FOR INTER-CLUSTER DATA DISSEMINATION IN VEHICULAR COMMUNICATION

A BROADCAST BASED ROUTING PROTOCOL FOR INTER-CLUSTER DATA DISSEMINATION IN VEHICULAR COMMUNICATION

Infrastructure based routing: Wu et al. (2013) proposed a moving direction and destination location based routing (MEDAL) algorithm, which takes the moving directions of vehicles and the destination location to select a neighbor vehicle as the next hop for forwarding data. Nzouonta et al. (2009) proposed a set of Road-Based Vehicular Traffic routing (RBVT) protocols, areactive protocol RBVT-R and a proactive protocol RBVT-P that leverage real-time vehicular traffic information to create paths consisting of successions of road intersections. Punithavathi and Duraiswamy (2010) proposed a Client-Server based mobile agent for fast reponse and information reteival. However their protocol requires more server units to store and backup the data. Though most of these algorithm ssupports both V2V and V2I communications, they requires all vehicles to store the periodic hello beacons of other vehicles and also depends on the support of intersections.
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Modelo matemático Two-echelon Capacitated Vehicle Routing Problem para a logística de distribuição de encomendas

Modelo matemático Two-echelon Capacitated Vehicle Routing Problem para a logística de distribuição de encomendas

Variações do 2E-CVRP também são encontradas na literatura. Crainic et al. (2009) apresentaram uma variação do 2E-CVRP, chamada de two-echelon, synchronized, scheduled, multi-depot, multiple-tour, heterogeneous vehi- cle routing problem with time windows (2SS-MDMT- VRPTW), ao tratar o gerenciamento da Logística Urbana. Esses autores desenvolveram um modelo e formulações ge- rais para a nova classe a partir de Programação Linear In- teira, mas não realizaram nenhum experimento computaci- onal para a mesma. Grangier et al. (2014) abordaram uma nova classe do 2E-CVRP, chamada two-echelon multiple- trip vehicle routing problem with sattelite synchronization (2E-MTVRP-SS) e utilizaram uma meta-heurística Adap- tive Large Neighborhood Search para resolução do pro- blema. Soysal et al. (2014) abordaram pela primeira vez a variação time-dependent em problemas 2E-CVRP, o Two- echelon Capacitated Vehicle Routing Problem with Time Dependent (2E-CVRPTD), assim como fatores que influen- ciam no consumo de combustível, como o tipo de veículo, a distância percorrida, a velocidade e a carga transportada pelo veículo. Esses autores desenvolveram um modelo ma- temético de PLIM baseada no modelo proposto por Jepsen et al. (2013) e testaram o modelo em um caso real, uma ca- deia de suprimentos localizada nos Países Baixos, com 1 depósito, 2 satélites e 16 clientes.
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Pesqui. Oper.  vol.35 número3

Pesqui. Oper. vol.35 número3

ABSTRACT. In this work we propose a multicriteria evaluation scheme for heuristic algorithms based on the classic Condorcet ranking technique. Weights are associated to the ranking of an algorithm among a set being object of comparison. We used five criteria and a function on the set of natural numbers to create a ranking. The discussed comparison involves three well-known problems of combinatorial optimization – Traveling Salesperson Problem (TSP), Capacitated Vehicle Routing Problem (CVRP) and Quadratic As- signment Problem (QAP). The tested instances came from public libraries. Each algorithm was used with essentially the same structure, the same local search was applied and the initial solutions were similarly built. It is important to note that the work does not make proposals involving algorithms: the results for the three problems are shown only to illustrate the operation of the evaluation technique. Four metaheuristics – GRASP, Tabu Search, ILS and VNS – are therefore only used for the comparisons.
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Contributions to the single and multiple vehicle routing problems with deliveries and selective pickups

Contributions to the single and multiple vehicle routing problems with deliveries and selective pickups

In the Single Vehicle Routing Problem with Deliveries and Selective Pickups (SVR- PDSP) there are a set of customers to be served and a depot from where a vehicle departs to serve the customers. Each customer has a certain demand of goods either to be delivered or to be picked up, which generates a revenue if collected. It is possible for a customer to have both demands. In such case, if both are going to be served, they can be performed simultaneously or in two different visits, each completely fulfilling one of the demands. The vehicle that departs from the depot shall perform a route that visits a subset of customers performing deliveries and pickups, then return to the depot. All delivery demands must be fulfilled exactly once. The pickup demands, however, are not mandatory, therefore they are only performed if there is enough space in the vehicle and if they are profitable. Serving a pickup demand is profitable if the revenue generated by collecting it is greater than the additional routing cost. One can notice that some pickups might not be served at all and it is possible to argue that they would need to be performed at some point. To address this issue these pickup demands could either be delayed to be served in the following day, or a third party service can be used to collect these pickups, which could be a less costly option than forcing all pickups to be fulfilled or sending another vehicle only to perform a few pickups. The objective is to find a route that minimizes the total routing cost, which is the travel cost to visit the customers minus the revenue generated by the collected pickups. Fig- ure 1.1a shows a small example with 8 customers and a vehicle with capacity equals to 35. In the figure, d is the delivery demand of a customer, while p stands for the pickup demand and r is the revenue generated by performing the respective pickup demand of the customer. The transportation cost of the solution presented is equal to 5 + 4 + 4 + 1 + 10 + 8 + 5 + 4 = 41 and the total revenue generated by the three pickups collected is 5 + 20 + 8 = 33. Therefore the total cost of this solutions is 41 − 33 = 8. In
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