# Top PDF A solution for a real-time stochastic capacitated vehicle routing problem with time windows

### A solution for a real-time stochastic capacitated vehicle routing problem with time windows

Stage 3. Post optimization - The next stage is a cycle which is computed until all routes are closed by the ERP/administrator or all PFIH parameters are tested. The cycle starts by getting (and removing) the most promising solution from P F IHSet and setting an ejection rate value. A tabu list, T , is started which will contain all computed solutions before applying post-optimization, for each ejection rate. Then try at most M axT ries times to improve the solution by applying: (a) a 2-Opt operator (which iterates through all routes, one by one, and tries to rearrange the sequence by which the customers are visited in order to reduce the route distance, maintaining feasibility [CGF + 08]); (b) a cross route operator (similar to the One Point Crossover operator of the Genetic Algorithms [MaM13], receives two paths as input, and tries to ﬁnd a point where the routes can be crossed, thus improving the total distance and without losing feasibility); and (c) a band ejection operator which is a generalization of the radial ejection [SSSWD00](selects a route and, based on the proximity and similarity of the nodes, for each customer located in the route ejects it and a certain number of geographical neighbors which are then reinserted in other routes, without violating the problem’s constraints). Please refer to [CSME15] for a more detailed explanation. The ﬁrst two operators are capable of diminishing the total distance, i.e., doing route optimization. However, they are not capable of reducing the number of routes present in the original solution, which can be achieved using the third operator. The ﬁrst two stages are quite fast. Therefore it is during the last stage that new orders arriving from the i3FR-Hub to the i3FR-Opt are treated. On other words, the i3FR-Opt/Server thread is responsible for the continuous communications with the i3FR- Hub, and whenever new orders arrive they are placed in shared memory. After each cycle the i3FR-Opt/Optimizer checks the shared memory for new orders that will be treated as ejected customers, i.e., it tries to insert them in the existing routes or creates a new route if that is not possible. As mentioned, during the process, improved solutions are sent from the i3FR-Opt to the i3FR-Hub which in turn resends them to the ERP/administrator.
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### A Clustering Approach for Vehicle Routing Problems with Hard Time Windows

The Vehicle Routing Problem is a well studied combinatorial optimization problem with many real practical applications. The variant of VRP that we considered is the VRPs with hard time windows. Literature is full with different methods for solving this type of VRPs. Exact methods for solving VRPs with optimalitty are based on Mixed Integer Programming (MIP) and are only capable of solving instances with no more than 100 requests. Approximation methods do not provide any guaranties on the solution quality, but are more efficient computationally. Empirical results show that approximation methods are capable of achieving solutions with quality within 1% of optimalitty. However, even approximation methods are computationally expensive due to the large search neighborhoods.
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### Multiple Charging Station Location-Routing Problem with Time Window of Electric Vehicle

A network is normally represented by a graph that is composed of a set of nodes and edges. The task of network clustering is to divide a network into different clusters based on certain principles. Each cluster is called a community. The LRP combines two classical planning tasks in logistics, that is, optimally locating depots and planning vehicle routes from these depots to geographically scattered customers [8]. These two interdependent problems have been addressed separately for a long time, which often leads to suboptimal planning results. The idea of LRP started in the 1960s, when the interdependence of the two problems was pointed out [9,10]. The variants of the LRP have been frequently studied in recent years. Such variants include the capacitated LRP (CLRP) with constraints on depots and vehicles [20,21], the LRP with multi-echelon of networks [11,12], the LRP with inventory management [13,14], and the LRP with service time windows [15–17]. For the variant problem with time windows, Semet and Taillard incorporated the time window constraint to the LRP for a special case of the road–train- routing problem [15]. Zarandi et al. studied the CLRP with fuzzy travel time and customer time windows, in which a fuzzy chance-constrained mathematical program was used to model the problem [16]. Later, they extended the problem by adding the fuzzy demands of customers and developed a cluster-first route-second heuristic to solve the problem [17]. A detailed review of the LRP variants can be found in two recent surveys [18,19].
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### 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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### Ant Colony Optimization for Capacitated Vehicle Routing Problem

The CVRP has been an attractive issue in the field of distribution and logistics which is motivated by both its practical relevance and its considerable difficulty. In this study, we have compared the solution quality of different basic heuristics combined with an original ACO in solving the problem. The computational results of fourteen benchmark problems shown that the ACO combined with the swap and 3-opt heuristic has the capability to tackle the CVRP with satisfactory solution quality and run time. Therefore it is a viable alternative for solving the CVRP.
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### 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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### 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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### Scatter search para problemas de roteirização de veículos com frota heterogênea, janelas de tempo e entregas fracionadas.

This work studies the implementation of heuristics and scatter search (SS) metaheuristic in a real heterogeneous fleet vehicle routing problem with time windows and split deliveries (HFVRPTWSD) in Brazil. In the vehicle routing problem with time windows and split deliveries (VRPSD) each client can be supplied by more than one vehicle. The problem is based in a single depot, the demand of each client can be greater than the vehicle’s capacity and beyond the time windows constraints, and there are also vehicle capacity and accessibility constraints (some customers cannot be served by some vehicles). The models were applied in one of the biggest retail market in Brazil that has 519 stores distributed in 12 Brazilian states. Results showed improvements over current solutions in a real case, reducing up to 8% the total cost of the operation.
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### Optimized Crossover Genetic Algorithm for Vehicle Routing Problem with Time Windows

This representation is unique and one string can only be decoded to one solution. It is a 1-to-1 relation. The last customer visited in route i is linked with the first customer visited in route i+1 to form a string of all the routes involved. Note that we do not put any bit in the string to indicate the end of a route now, because such delimiters in a string greatly restrain the validity of children produced by optimized crossover operator later. To decode the string into route configurations, the gene values are inserted into the new routes sequentially.

### Route Elimination Heuristic for Vehicle Routing Problem with Time Windows

information about the graph is in the nodes and we know that it is impossible or at least not worthy– according to our present knowledge and computers – to reveal all the relationships within the nodes if the number of nodes is above 50. Suppose that we have an initial solution. Let the width of a brick the distance – cost – between two nodes on any route and the waiting time is the gap between the bricks. Similarly a single route can be considered a row of bricks in the wall and the whole number of routes would create a wall. Now the objective of VRP can be redrafted: rebuild the wall to get primarily smaller wall – with fewer routes – and secondly try to reduce the length of the brick-rows. We can easily recognize the unique behaviour of this wall, because if we swap any two bricks in the wall each of them changes its width and maybe the following gaps as well. Moreover, not only the two bricks but their predecessors in the rows change similarly, because their neighbours are changed. So swapping two bricks at least four brick widths will change and in bad situation many gaps are affected. From this respect this wall is exactly as complicated as the graph itself, but if we think of the route elimination we can identify its requirements more clearly. If we select a row for elimination:
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### REAL-TIME MONITORING SYSTEM USING UNMANNED AERIAL VEHICLE INTEGRATED WITH SENSOR OBSERVATION SERVICE

One additional Usb-Hub has been added for connecting devices since there is not enough port in Mini PC. Gyrocompass and Laser range meter are connected directly to the system using serial interface (RS232) due to performance issue. USB ports are used for connecting a digital camera and a webcam to the PC. GPS, ARM controller board and A/D controller board also used USB interface but they needed an additional converter, USB-to-Serial for connecting because they were native serial devices. All devices that used converter will be mapped as virtual com port in the system. Wireless card was added inside Mini PC to allow wireless connection. In our experiment, when we connected gyrocompass with USB-to-Serial interface, it consumed a lot of CPU time; however, when we connected it using native serial port, it worked fine and did not consume much of CPU time. For Digital camera and Webcam, they were connected directly to mini PC to reduce latency in data transferring between system and Usb-Hub. Also they would not interrupt other devices when transferring huge data. In figure 5 and 6, it shows layout of the equipment on the platform. The Platform was made from aluminium that had less weight than metal. It was also drilled to make many holds to make it more light-weighted and to have better air flow (Nagai et al., 2009). Gyrocompass was put in the direction pointed to front side of helicopter to make it possible to know directions of the helicopter when flying and it was also orthogonal to the platform. Webcam was put in same direction with gyrocompass to get a clear front view. It was attached on pan-tilt stand that can be controlled via software. Laser range meter and camera are put in the same line and point down to the ground view so that we knew the distance between camera position and the ground. Wireless antenna was put in the right side of the platform. It was a removable antenna so it would be switchable to a bigger one for long range observation. With the 2200 mA Li-Po battery, system can run up-to one hour. All equipments were using the main battery except the camera and the laser range meter that used their internal AA battery.
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### A complete internet of things solution for real-time web monitoring

most widely used ones, and those specifically designed for IoT. As it will have a major impact in the health sector, a testbed was set to demonstrate how real IoT systems can currently be deployed using open standards and open-source tools. With this testbed implementation and validation, one can conclude that an IoT architecture should be as generic as possible, meaning that it should be designed to cover the most different use cases typically found in IoT and M2M. The real-time monitoring web-based solution, found in this testbed, is a prime example of how such flexibility is needed. If this solution had not been well adapted to meet the real-time requirement, the IoT architecture adopted (ETSI M2M) would not be able to deliver such a service. This is something of utmost importance and that is something the next generation of IoT standards (oneM2M, specifically) will tackle, by combining even more technologies into the standard (like device management via LWM2M, for example) and protocol bindings like MQTT. Having this standard architecture with different horizontal layers offering fundamental functionalities, while being network agnostic, is a major breakthrough and will allow the IoT to fully grow up to its potential. Interacting with the services via a standardized RESTful API will make way for new Web and mobile based applications to grow and directly interact with the already well established Internet. In the end, this will, ultimately, result in smarter, fully aware IoT applications which combine different data sources and mechanisms to trigger events in other IoT systems/applications, offering the end-user a better quality of life with simplified, smart and autonomous real-life actions.
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### A mathematical modeling proposal for a Multiple Tasks Periodic Capacitated Arc Routing Problem

The countless accidents and incidents occurred at dams at the last years, propelled the development of politics related with dams safety. One of the strategies is related to the plan for instrumentation and monitoring of dams. The monitoring demands from the technical team the reading of the auscultation data, in order to periodically monitor the dam. The monitoring plan of the dam can be modeled as a problem of mathematical program of the periodical capacitated arcs routing program (PCARP). The PCARP is considered as a generalization of the classic problem of routing in capacitated arcs (CARP) due to two characteristics: 1) Planning period larger than a time unity, as that vehicle make several travels and; 2) frequency of associated visits to the arcs to be serviced over the planning horizon. For the dam's monitoring problem studied in this work, the frequent visits, along the time horizon, it is not associated to the arc, but to the instrument with which is intended to collect the data. Shows a new problem of Multiple tasks Periodic Capacitated Arc Routing Problem and its elaboration as an exact mathematical model. The new main characteristics presented are: multiple tasks to be performed on each edge or edges; different frequencies to accomplish each of the tasks; heterogeneous fleet; and flexibility for more than one vehicle passing through the same edge at the same day. The mathematical model was implemented and examples were generated randomly for the proposed model's validation.
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### 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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### A Study on Speech-Controlled Real-Time Remote Vehicle On-Board Diagnostic System

The OBD system can consecutively monitor the running condition of vehicle. Once there is a malfunctioning element that controls the emission of exhaust, the OBD system will turn on the Malfunction Indicator Lamp (MIL) or the Check Engine light in the in-car dashboard to notify the driver to repair the car immediately. When the OBD system detects a malfunction, OBD regulations will inform the Electronic Control Unit (ECU) of the vehicle to save a standardized Diagnostic Trouble Code (DTC) about the information of malfunction in the memory. An OBD Scan Tool for the servicemen can access the DTC from the ECU to quickly and accurately confirm the malfunctioning characteristics and location in accordance with the prompts of DTC that shortens the service time largely. It follows from SAE standards [1]-[3], currently OBD can monitor more than 80 items of real-time driving status, e.g., vehicle speed, engine rpm, engine coolant temperature, battery voltage, and etc.
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### Real-Time Evaluations

An RTE should be launched as soon as a new emergency has occurred or appears imminent. Initially, the evalu- ators will be active participants in the crisis cell established for the emer- gency, collecting and reviewing relevant documentation on a system- atic basis. Evaluation teams interview senior managers and establish contact with key offices and personnel in the field. The evaluators’ role is not sim- ply a passive or analytical one as they use their skills and experience to pro- vide advice to emergency managers and alert them to impending prob- lems. Before they leave a field location an RTE team holds an interactive debriefing with UNHCR staff and, if possible, with representatives of part- ner organisations. Back at HQ they quickly provide debriefings to the High Commissioner and other mem- bers of senior management, to Executive Committee members and to NGOs.
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### Formulation and Solution ofTwo-Level Capacitated Lot-Sizing Problem

This paper considers a production lot-sizing and scheduling of the problem encountered in a soft drink bottling plant.The main challenge is how to determine the lot-sizing and scheduling of raw materials in the tanks of soft drinks in bottling lines simultaneously, with sequence-dependent setup costs and times and production capacity constraints at each level so that the total production costs are minimized. Chen and Thizy [1]proposed a multi-item capacitated lot-sizing problem consists of determining the magnitude and the timing of some operations of durable results for several items in a finite number of processing periods to fulfil a known demand in each period. The proposed model was solved by a Lagrangian relaxation. Fleischmann [2]presented discrete lot-sizing and scheduling problem consists in scheduling several products
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### Ant colony optimization techniques for the hamiltonian p-median problem

Furthermore, for attainment of improved solutions to the problem, we can use the route improvement strategies in the algorithms. One of these strategies involves the inclusion of a local exchange procedure to act as an improvement heuristic within the routes found by individual ants. One of the techniques used for this purpose is the common 2-opt heuristic where all possible pairwise exchanges of customer locations visited by individual vehicle are tested to see if an overall improvement in the objective function can be attained.

### Problema de Roteamento de Veículos – Vehicle Routing Problem (VRP)

geograficamente dispersas, encontrar o melhor conjunto de rotas para atender todos os. clientes[r]

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### Real-Time intelligence

Column-oriented databases are the kind of data store that most resembles the relational model on a conceptual level. They retain notions of tables, rows and columns, creating the notion of a schema, explicit from the client’s perspective. However, the design princi- ples, architecture and implementation are quite different from traditional RDBMS. While the notion of tables’ main function is to interact with clients, the storage, indexing and dis- tribution of data is taken care by a file and a management system. In this approach, rows are split across nodes through sharding on the primary key. They typically split by range rather than a hash function. This means that queries on ranges of values do not have to go to every node. Columns of a table are distributed over multiple nodes by using “column groups”. These may seem like a new complexity, but column groups are simply a way for the customer to indicate which columns are best stored together Cattell (2011). Rows are analogous to documents: they can have a variable number of attributes (fields), the attribute names must be unique, rows are grouped into collections (tables), and an individual row’s attributes can be of any type. For applications that scan a few columns of many rows, they are more efficient, because this kind of operations lead to less loaded data than reading the whole row. Most wide-column data store systems are based on a distributed file system. Google BigTable, the precursor of the popular data store systems of this kind, is built on top of GFS (Google File System).
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