Top PDF Vehicle routing problem with time windows in the distribution of steel products

Vehicle routing problem with time windows in the distribution of steel products

Vehicle routing problem with time windows in the distribution of steel products

Regarding added value to the company, as it has been raised, this research project had the virtue of having collected a large amount of data and turning it into knowledge with implications on daily decisions. Today, for instance, each truck is linked to a cost per ton delivered, as well as a number of kilometers to distribute one ton. Moreover, the cost structure of each truck is detailed from insurance and fines to fuel, tolls and maintenance. This is important to control not only the performance of each truck, but also of each driver. Furthermore, the detailed level of understanding of the distribution planning allowed to identify a set of problems that the company will have to properly manage in order to improve outbound logistics. The evolution in the departure time of the drivers in Ovar, which is a good business unit from this point of view, is illustrative of the difficulties that challenge the output of this process. Additionally, the credit notes related to drivers’ lack of time are also a symptom of the problems identified.
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The multi-compartment vehicle routing problem in the collection of recyclable municipal solid waste

The multi-compartment vehicle routing problem in the collection of recyclable municipal solid waste

The MCVRP is seldom studied in the literature. With the development of heuristics and the effort to improve them, the quality of the MCVRP solution increases, making it more appealing to the be implemented in the industry of waste (or any other). Only with more research on this topic the real advantages and disadvantages of the use of vehicles with multi-compartments can be perceived. Some improvements or additional studies are indicated in this chapter for future research in order to enhance the implementation of the MCVRP and of the proposed heuristic. First of all, it would be pertinent to analyse if the acquisition of vehicles with multi- compartments are economically feasible for Valorsul, or, in alternative, the adaptation of the existing vehicles (in-house or not). Secondly, the quality of the data should also be a focus for the company. Worrell et al. (2011) stated that the holy grail in the world of MSW is to be able to determine how much waste is generated; to do so, the filling level of each container must be correctly measured every time each one is emptied. Since the current method is not accurate, Valorsul may study the possibility of implementing a system (as RFID chips, which are already used) to record the exact amount of waste collected per container.
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Modelos matemáticos baseados no Time Dependent Vehicle Routing Problem para planejamento da logística urbana sob a ótica ambiental

Modelos matemáticos baseados no Time Dependent Vehicle Routing Problem para planejamento da logística urbana sob a ótica ambiental

Abstract: Urban logistics companies are seeking solutions to reduce their cost, but must of them are not paying attention to environmental issues. This is due to the belief that environmentally friendly solutions are more expensive. However, with the growing of environmental concerns, companies have been taking into account the environmental factors, seeking for their social responsibility. Thus, this paper presents two mathematical models, both based on the Time Dependent Vehicle Routing Problem (TDVRP), one to evaluate the reduction in the time of the routes and the other to evaluate the reduction of greenhouse gas emissions. In order to evaluate the model, a real case of a food distribution company in the metropolitan area of Vitória, ES was done. CPLEX 12.6 was used to run both models considering scenarios based on data from a real company. The results showed that environmentally friendly solution may be also financially advantageous for the company.
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Contributions to the single and multiple vehicle routing problem with deliveries and selective pickup

Contributions to the single and multiple vehicle routing problem with deliveries and selective pickup

The only approach for the MVRPDSP in which we must perform an analysis to calibrate its parameters is the hybrid constructive heuristic. In its routing phase the heuristic tspKnapsackBased from the SVRPDSP is used. Notice that this heuristic has a parameter rclSize and as in the SVRPDSP it must be calibrated. For that end we ran 30 times the hybrid constructive for all instances using each rclSize value from the set of values {1, 2, 3}. The best solution values were normalized and an ANOVA was performed on these values. The results of such analysis are shown in Figure 5.4a. Clearly the rclSize = 1 is the best with 95% of confidence. However, since for the SVRPDSP the rclSize = 2 proved to be also a good choice, we decided to perform an ANOVA only considering our biggest instances, with size 72. The results can be seen in Figure 5.4b and shows that the difference between the rclSize = 1 and rclSize = 2 is much smaller and the rclSize = 1 is no longer proved to be the best. This behavior could be due to the fact that in bigger instances the solutions tend to have more customers per route, which greatly increases the number of possibilities to insert the pickup customers in the routing phase. Therefore, we foresee that for instances with a greater number of customers the value rclSize = 2 may be equally a good choice or even a better one. Nevertheless, since the constructive is not time consuming we decided to use both, rclSize = 1 and rclSize = 2, then choose the best solution generated.
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A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls

A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls

The goal of the MDVRPB is to determine the routes to be performed from the selected depots to the customers by a fleet of homogeneous vehicles in order to satisfy the demand of the customers (products to be collected or products to be delivered). The objective functions considered for the multiobjective version of the MDVRPB is to minimize the total traveled distance, the total time and the consumed energy. The first objective is the common function considered in the literature related to the vehicle routing problems. The second objective is obtained by the allowed speed on each edge. In particular, we have considered a random speed between 30 km /hr to 90 km/hr for the complete graph on the benchmarking set of instances. Finally, the third objective is adopted from the idea of gas emission and consumption of energy introduced by Bektaş and Laporte (2011) and Demir et al. (2014).
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Ant Colony Optimization for Capacitated Vehicle Routing Problem

Ant Colony Optimization for Capacitated Vehicle Routing Problem

Since the CVRP is a NP-hard problem, only instances of small sizes can be solved to optimality using exact solution methods (Toth and Vigo, 2002; Baldacci et al., 2010). As a result, heuristic methods are used to find good, but not necessarily guaranteed optimal solutions using reasonable amount of computing time. Starting with the simple constructive approaches such as the savings algorithm proposed by Clarke and Wright (1964) or basic improvement methods such as the 2-opt heuristic, the general-purpose heuristic methods (which are called metaheuristics) have then been developed to guide subordinate heuristics to avoid or overcome local optimality. During the past two decades, an increasing number of literatures on heuristic approaches have been developed to tackle the CVRP. The summary and discussion of several important and state-of-the-art modern heuristics for the problem can be found in the study by Cordeau et al. (2002) and Szeto et al. (2011).
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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

These results are somehow expected due to the fact that the algorithm No.2 does not give always the same route for trucks requiring the same locations. By choosing always the least occupied server at the time of reaching the servers and by computing different routes, avoiding the congested ones, the facility reaches a higher level of equilibrium. Usually, when computing a system that is static (always giving the same route, in the case of the algorithm No.1, the minimum distance one), there will be one (or several) road(s) and server(s) that will represent the so-called bottleneck(s). The algorithm No.2 tries to equilibrate the occupation both in the servers and in the roads, not overloading any of them, thus eliminating these bottlenecks or, at least, mitigating its effects. Besides the results of the simulations, it was possible to observe that, in the simulations using the algorithm No.2, the trucks were much more dispersed inside the facilities, decreasing the queues in the servers, per example. Also, it was possible to observe that, in some cases, the algorithm No.2 has chosen some roads that in the simulations of the Algorithm No.1 had not been used at all. In the roads case, as the sets have a limited and relatively lower number of trucks, the division may not be so present. However, in a day to day of a factory, with hundreds of trucks, the roads would become much more congested. In that case, the algorithm No.2 will provoke a much more highlighted division of the trucks in the roads.
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Towards solving a robust and sustainable Vehicle Routing Problem with Backhauls

Towards solving a robust and sustainable Vehicle Routing Problem with Backhauls

This paper studies the integration of inbound and outbound logistics in the context of the wood-based panel industry. The case study is driven from a real-life industrial application that operates on a multi-mill setting. The production strategy of the wood-based panels at each mill is Make-to-Order. The finished products are shipped to the customers in the day after its production. The stock of raw materials should be at least one week to overcome fluctuations in wood supply. The outbound logistics are planned locally, in the transporta- tion department of each mill, while the inbound logistics are planned centrally, considering the bulk demand for all the mills. The goal here is to find daily minimum-cost outbound and inbound routes (OIRs) where the vehicle departing from each mill firstly performs a sequence of deliveries of the amounts ordered by the customers, and secondly, whenever is cost-effective, picks up a full truck-load of raw materials at a nearby supplier, and de- livers it at the closest company’s mill. OIRs allow better use of the delivery truck, when compared with ORs and further avoid dedicated IRs. This is possible because the driver can easily adapt the same truck that transported the wood boards with reinforcements in its structure so it can transport a full truck-load of wood chips. For wood-based supply chains,
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A study of flow-based models for the electric vehicle routing problem

A study of flow-based models for the electric vehicle routing problem

The columns represent, from left to right, the model used, the linear pro- gramming relaxation value (LR), the optimal value (OPT), the linear gap, the time taken in seconds to optimality and the number of branch-and-bound (B&B) nodes in the final tree. In the models used section we use the legend CCs to refer to the Connectivity Cuts, separated using algorithm 4.1, and CPCs to refer to the CPLEX integrated cuts, which can not be controlled by us in any form except choosing to remove them and can lead to erratic behavior. Note that the linear programming relaxation values are calculated without any pre-processing done on the model by CPLEX although to solve to optimality pre-processing is allowed. Basically, the program developed calculates the linear programming relaxation of the model first with the pre-processing turned off and then restarts the branch-and-bound process with pre-processing turned on.
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A matheuristic for the consistent vehicle routing problem with service level agreements: a case study in the pharmaceutical distribution sector

A matheuristic for the consistent vehicle routing problem with service level agreements: a case study in the pharmaceutical distribution sector

This research mainly focuses on a less studied VRP extension which is the conVRP. This optimization problem demands the definition of vehicle routes for several periods, maintaining a certain level of consistency on pre-selected metrics. For instance, when distribution companies make an agreement for the deliveries to be made always by same driver, they are adding consis- tency constraints in order to take into account customer satisfaction. Therefore, the objective is to achieve minimum cost routing plans satisfying the classical routing constraints as well as con- sistency requirements taking into account customer satisfaction. Generally, this type of customer- oriented routing considers two types of consistency for customer satisfaction: driver consistency, and time consistency (Kovacs et al., 2014a). Driver consistency is measured by the number of different drivers that visit a customer whereas time consistency is related to the maximum dif- ference between the earliest visit and the latest arrival at each customer. The conVRP arises in many industries where customer satisfaction is considered as a distinctive factor of competitive- ness. Particularly in industries transporting small packages, providing a standard service with a single driver and approximately at the same time of the day enables the customers to prepare them- selves for a delivery, strengthening supplier/customer relationships (Kovacs et al., 2014b). Since the conVRP considers several periods, it can be seen as a tactical extension of the classical VRP with customer-focused routes.
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Planning the distribution of agricultural products in a short distribution channel

Planning the distribution of agricultural products in a short distribution channel

In this chapter, we present the computational results for the integrated approach and for the unaggregated approach. Seven instances, based on the case study, and the case study itself were tested. As for the former instances, dierent combinations of the number of customers and farmers were dened (Table 7.1). The number of fresh and storable products is always the same for these instances, three and fteen, respectively. In order to solve the presented MILP model, a free MILP solver software lpsolve 5.5.2.0 [17] was embedded in the developed software. This solver would be able to create the appropriate model and to solve it but, due to the excessive CPU time needed to solve even the smallest instance, it was established that the model would be constructed using lpsolve but it would be solved by the commercial CPLEX 12.5 [5] instead. Instances 1, 2, 3, 6 and 7 were solve to optimality, but in order to obtain a feasible solution for instances 4, 5 and the test case, a CPU time limit of one hour was established. The presented results for the MILP model were obtained using a desktop computer with an Intel Core 2 - 2.13 GHz processor and 2 GB RAM, and a Windows 7 Professional operating system.
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Application of Harmony Search to Vehicle Routing

Application of Harmony Search to Vehicle Routing

A recently-developed music phenomenon-inspired algorithm, HS was introduced and modeled for solving the school bus routing problem. The objective of proposed HS model for the school bus routing is to minimize the total cost of multi-objective function which consists of bus operating cost, bus travel time, and penalties related with bus capacity and time window violations. HS model could find global optimum within far less function evaluations comparing with total enumeration. HS model also found better solution than GA in terms of number of reaching global optimum, average cost out of multiple runs, and computing time.
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A NOVEL TRANSITION IDENTIFICATION MECHANISM FOR THE DIESEL BLENDING AND DISTRIBUTION SCHEDULING PROBLEM USING THE DISCRETE TIME REPRESENTATION WITH TWO TIME-SCALES GRANULARITY

A NOVEL TRANSITION IDENTIFICATION MECHANISM FOR THE DIESEL BLENDING AND DISTRIBUTION SCHEDULING PROBLEM USING THE DISCRETE TIME REPRESENTATION WITH TWO TIME-SCALES GRANULARITY

In principle, for long time horizons, the idea that products are shipped only once along the entire time horizon cannot be sustained since multiple shipments are unavoidable because demand is distributed along the time horizon and pipelines are capacitated. In other words, there may be multiple due dates for the same product and, most likely, it will not always be possible to lump parcels so as to fulfill multiple demand incidences because of pumping capacity. On the other hand, enforcing a maximum number of shipments for each product establishes the maximum number of events or shipments that may happen within a time interval, which sets an upper bound on the number of transitions. In order to take advantage of this fact, the idea was to create two levels of granularity for time, e.g., the scheduling horizon could be subdivided in days within which products could be allowed to be shipped only once, which in turn could be subdivided in hours to accommodate allocation of multiple time periods. Bottom line, with the two levels of aggregation, transitions would be managed on a higher level of detail and hence a few constraints would be required to create all combinations of product interfaces and product allocation would be managed on the lower level of aggregation.
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An adaptive large neighbourhood search for the operational integrated production and distribution problem of perishable products

An adaptive large neighbourhood search for the operational integrated production and distribution problem of perishable products

Production and distribution problems with perishable goods are common in many in- dustries. For the sake of the competitiveness of the companies, the supply chain planning of products with restricted lifespan should be addressed with an integrated approach. Par- ticularly at the operational level, the sizing and scheduling of production lots have to be decided together with vehicle routing decisions to satisfy the customers. However, such joint decisions make the problems hard to solve for industries with a large product portfolio. This paper proposes an adaptive large neighbourhood search (ALNS ) framework to tackle the problem. This metaheuristic is well-known to be effective for vehicle routing problems. The proposed approach relies on mixed-integer linear programming models and tools. The adap- tive large neighbourhood search outperforms traditional procedures of the literature, namely exact methods and fix-and-optimize, in terms of quality of the solution and computational time of the algorithms. Nine in ten runs of ALNS yielded better solutions than traditional procedures and the best solution value found by the latter methods 12.7% greater than the former, on average.
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The crew scheduling and routing problem in road restoration

The crew scheduling and routing problem in road restoration

Extreme events as large-scale disasters can cause partial or total disruption of basic ser- vices such as water, energy, communication and transportation. In particular, recovering the transportation infrastructure is of ultimate importance in post-disaster situations, to enable the evacuation of victims and the distribution of supplies to affected areas. Road restoration, one of the main activities in this context, is a complex activity due to its inherent decisions that must be taken quickly and under uncertainty, such as the allocation of resources and the schedul- ing/routing of the crews that perform the restoration activities. In this thesis, we address road restoration by means of the Crew Scheduling and Routing Problem (CSRP), which integrates scheduling and routing decisions. The problem also involves the design of relief paths to con- nect a supply depot to demand nodes that become accessible only after the damaged nodes in these paths are repaired. We start addressing the basic variant of the CSRP, which considers a single crew available to perform the repair operations and minimizes the accessibility time of the demand nodes. Then, we extend the problem to consider multiple heterogeneous crews and uncertainties in the repair times via robust optimization. Also, we introduce the minimization of the latency of the demand nodes, where the latency of a node is defined as the accessibility time plus the travel time from the depot to that node. To solve the CSRP and the proposed ex- tensions, effective solution methods based on Benders decomposition are proposed. We propose three types of solution approaches: branch-and-Benders-cut algorithms (BBC), metaheuristics based on simulated annealing and genetic algorithm, and hybrid branch-and-Benders-cut algo- rithms (HBBC). We develop two BBC algorithms. The first BBC has a master problem with scheduling decisions while the crew routing and the design of relief paths are considered in the subproblems. The second
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A mixed load rural school bus routing problem with heterogeneous fleet: a study for the Brazilian problem

A mixed load rural school bus routing problem with heterogeneous fleet: a study for the Brazilian problem

The MLI procedure of Park et al. (2012) does not take into account the routing costs, having the only objective of minimizing the number of used buses. Park et al. argue that the routing costs can be disregarded because they are much smaller than the fixed bus costs. Unfortunately this is not true for all the situations. Given the lifespan of a bus and as the picking and delivery of students are routinely done over the school year, on the long run, the routing costs may start to weight on public spendings. Further, when they are not taken into account, some collateral distortions may occur. For instance, the average weighted riding distance of the students may be much larger than when these costs are embedded into the solution process. Recall that the average weighted riding distance of the students is the sum of all of the traveled distance of each student from his bus stop to his school divided by the total number of pupils. These effects are more widely perceived when schools do not have the flexibility of having different starting times, as it assumed in the study of Park et al. (2012). Due to labor policies and union treats, schools in Brazil have to start at the same time. To show the influence of these effects, the PARK dataset was solved by the ILS-RVND with mixed load (ML) of students, and by a modified version of the ILS-RVND allowed to handle only single load (SL). The attained results were compared with the ones obtained by the MLI procedure configured to disregard time windows. The same homogeneous fleet of Park et al. with capacity of 66 seats was used during the experiments. The fixed and the routing costs per unit of traveled distance were set to $200 and $1.00, respectively. Each instance of the PARK dataset was solved 30 times by the ILS-RVND-ML and ILS-RVND-SL with a different seed for each run.
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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

In this article, a vehicle routing with backup provisioning approach is proposed for sustainable urban mobility with efficient use of resources. Besides formalizing mathematically the problem, a heuristic is proposed that allows solutions to be obtained more quickly. The vehicle routing with backup provisioning approach is able to provide higher quality of service, regarding time for the backup vehicle to arrive, and it avoids new schedules/vehicles/drivers for backup provisioning. Although routes become longer, to ensure backup, thresholds on time for backup to arrive can be adequately set to keep such distances acceptable. However, since more stops are being served, the increase of routes should not be seen just as a penalty. Regarding the neighborhood formation approach and local search procedures, incorporated in the heuristic, these have proven to be effective. Route distances reduced by approximately 30%. In summary, the overall perception is that the proposed heuristic is able to effectively solve the vehicle routing with backup provisioning problem under consideration. As future work, we expect to study fleet planning considering vehicles of different sizes. Acknowledgments: This work was supported by FCT (Foundation for Science and Technology) from Portugal within CEOT (Center for Electronic, Optoelectronic and Telecommunications) and the UID/MULTI/00631/2013 project.
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Optimization Algorithms for the Inventory Routing Problem

Optimization Algorithms for the Inventory Routing Problem

• Particle Swarm Optimization (PSO)- PSO is a stochastic population-based metaheuris- tic Talbi ( 2009 ) in which problem solving system mimic collective intelligence in nature ( Alam et al. ( 2015 )). Each particle in a population represents a possible solution to the problem and is defined by its position where each position has its fitness evaluated by the optimized fitness function ( Sethanan and Neungmatcha ( 2016 )) and velocity which redirects its movement (replicating birds behavior when flying). Each particle adjusts it is position taking into account two previous solutions: the best personal value and the best populational value ( Talbi ( 2009 )) which are also kept in memory and thus allowing a quick convergence to global optima ( Lynn and Suganthan ( 2015 )). In Belmecheri et al. ( 2013 ) is presented a PSO algorithm with a local search for the vehicle routing problem with heterogeneous fleet, mixed backhauls and time windows (HVRPMBTW), which when comparing its results with an exact method and best know solutions of the literature for specific VRPTW problems, returns that HVRPMBTW achieves optimal solutions for small problems and was able to improve the GAP, which measures the distance to the optimal solution, by over 5.5% with 29 of 56 cases displaying its quickness in convergence, few parameter settings and fewer memory needed.
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Estimating the efficacy of mass rescue operations in ocean areas with vehicle routing models and heuristics

Estimating the efficacy of mass rescue operations in ocean areas with vehicle routing models and heuristics

The concept of layered graph in network design problems is usually associated to formulations and has gained widespread attention in the recent years. New formulations for several network design problems based on layered graphs have been proposed recently which show computational advantage over previous ones. To the best of our knowledge, the first reference to a formulation based on a layered graph is due to Picard and Queyranne [169] who proposed an integer linear programming formulation based on a multipartite graph for the time-dependent travelling salesman problem (TDTSP). They presented a branch-and-bound algorithm based on this formulation to minimize the tardiness cost in one-machine schedulling problem. The formulations for the TDTSP based on layered graphs proposed by Picard and Queyranne have been widely studied and several families of valid inequalities have been proposed to be used in branch-and-cut algorithms (see [170], [171], [172], [173]). Another early reference to layered graphs can be made by interpreting the network flow formulation by Steward [174] for the problem of optimal allocation of search effort. In this problem the purpose is to find an optimal allocation of search effort (effort can be measured by the time a sensor is searching for a target in a specific region or area) considering that a target moves between a set of cells during a finite set of periods of time according to a specified Markov process. The target path through time is given by a probability vector and if the sensor and target meet in the same cell then the probability of detection, given that the target is not detected earlier, is an exponential detection function similar to the one considered in Stone [32]. Eagle and Yee [175] propose a branch and bound algorithm for this problem, with the bound calculated by solving a relaxed problem using the Frank-Wolfe method [176]. Improvements on the Eagle and Yee procedure for this problem were made by Martins [177] where an improved bounding procedure was based on the solution of a single longest path problem that maximizes the expected number of detections.
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The mixed capacitated arc routing problem with non-overlapping routes

The mixed capacitated arc routing problem with non-overlapping routes

Real world applications for vehicle collection or delivery along streets usually lead to arc routing problems, with additional and complicating constraints. In this paper we focus on arc routing with an additional constraint to identify vehicle service routes with a limited number of shared nodes, i.e. vehicle service routes with a limited number of intersections. This constraint leads to solutions that are better shaped for real application purposes. We propose a new problem, the bounded overlapping MCARP (BCARP), which is defined as the mixed capacitated arc routing problem (MCARP) with an additional constraint imposing an upper bound on the number of nodes that are common to different routes. The best feasible upper bound is obtained from a modified MCARP in which the minimization criteria is given by the overlapping of the routes. We show how to compute this bound by solving a simpler problem. To obtain feasible solutions for the bigger instances of the BCARP heuristics are also proposed. Computational results taken from two well known instance sets show that, with only a small increase in total time traveled, the model BCARP produces solutions that are more attractive to implement in practice than those produced by the MCARP model.
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