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17 results for “Dynamic Routing”
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
Global monthly water temperature dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre> <p>V1.1 update includes a improved version of the model removing some initial spikes related to rapid ice melt and streams that fall dry. The record has been reduced from 1981 tot 2014 to remove potential spinup impacts.</p> <p>The 1960-2010 data from v1.0 can be used for the earlier years.</p> <p>Consistent forcing is used for both time periods to remove potential biases that might occur otherwise.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 9. Step by step drawing of the selected route solution
<p>When the solution that is desired to be viewed is double clicked, the connections between bus stops are drawn in turn, and the route is shown as can be seen in Figure 9.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 7. Application main form which includes route, bus stops and GA parameters
<p>For this study, the school bus routes within the Ankara Province were used as case studies. The school bus routes were recorded by using the Android application and instantaneous GPS monitoring method. The home address of each student was taken as a stopping point. At the end of each route, the distances between the beginning and end points were recorded. After transferring the obtained route data into the database, the ill-adapted points were eliminated. By means of the developed application, the existing school bus routes are dynamically optimized using GA. It was developed both as a mobile and desktop application. Using Android- based mobile software, the information regarding GPS locations of bus stops and school buses is transferred to the server on a real-time basis. Using the desktop software, where GAs are run, these coordinates are shown on a Google map, and the most suitable route is produced and sent to the school bus via server. This method provides the opportunity to dynamically reflect certain factors such as a different initial point for the school bus, some students being absent from the school on a particular day, and eventual changes on the existing roads on the route production process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process
<p>The individuals obtained at the end of crossing over might not provide the desired level of variability. In that case, the produced individuals are mutated independently from another individual in such a way that their own gene sequence will change. The mutation process is performed in the event that the mutation possibility that is specified in the beginning comes true. The results obtained from mutation can enhance the outcome or make it worse. It is of utmost importance to specify the most suitable mutation possibility. This possibility should be high enough to prevent the method from becoming stuck at a local point, but at the same time, low enough to allow the best results produced by crossing over and multiplexing. In this study, the mutation possibility was selected as 10%, and the locations of two randomly selected bus stops were changed during the mutation process. As in the crossing over, also during this process, the limitations regarding producing a new individual (route) were adapted. Figure 6 shows an example to mutation process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 4. Example of a chromosome structure with permutation coding
<p>Each chromosome found in the population formed in the GA is structurally an equal-length coded series. The chromosomes are made of genes. For coding purposes, binary, permutation, and value coding methods are widely used. In the travelling salesman or other similar VRPs, permutation coding technique is preferred over the other techniques. Using the permutation coding technique, each chromosome found in the population is expressed in terms of the numbers of each stop to be followed in the route, as shown in Figure 4.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 3. The flow diagram of the SBRP solution by using GA
<p>The distance optimization needed for the formation of the objective function that can be seen in equation number 1 was done using GA operators and parameters. The flowchart that can be seen in Figure 3 shows how the school bus routes are formed using GA.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 10. Obtained experimental results
<p>The GA procedure was carried out using the parameter values that can be seen in Table 1, for a total of 10 school bus services’ routes identified by means of mobile-based software for a school located in the Ankara Province. The obtained experimental results are shown in Table 2 and Figure 10. The algorithm working duration also includes the formation of a distances’ matrix.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 1. Example of a VRP solution
<p>VRP is widely described as the process where the distribution vehicles deliver goods in a depot to the clients found in geographically dispersed locations and then return to the depot on the optimum route. The VRP, first introduced by Dantzig and Ramser in 1959, aims to minimize the total distance to be covered during the routing of the vehicles in a centrally-located depot (Dantzig & Ramser, 1959). The routing process is operated by taking the vehicle capacities into consideration so as to ensure that each client located on the routing plan is visited only once. The depot, vehicle, and goods concepts mentioned in the general description of VRP can be re-adapted as school, student and bus stop, based on different sectors that the problem applies to. Figure 1 shows a scheme indicating the VRP solution applied to a distribution system, where the clientele network is dispersed over three different zones.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 8. Listing of obtained solutions after running GA on the route
<p>During the route planning, school is the initial point if the students are going home from school, whereas it is the final point if they are going to the school from their homes. The application provides the opportunity to fix not only the school, but also the bus stops as initial or final points. Since the authors hope to develop a routing solution for more than one school in a future study, both the initial and final bus stops were given the opportunity to be selected so that the school bus can complete distribution/collection duties for one school and can go to another school for routing. When these selections are made, the initial and final genes of the chromosomes produced in the population were fixed through these selections. When the TSP option is on, on the other hand, the school bus returning to the initial point after completing the distribution/collection duties is included in the routing process. The information regarding solutions, route distances and the number of iterations produced after all the parameters are defined and the relevant selections are made are listed as shown in Figure 8. Under the solutions list, total crossover, total number of mutations, and algorithm working times are shown.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process
<p>In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 2. A dynamic school bus routing case
<p>VRP’s can also be classified into two categories as dynamic and static routing problems. In the static VRP’s, the stops/locations that the vehicle will visit are pre-specified and do not change during the distribution/collection process. In dynamic VRP’s, on the other hand, new stops can be added to the planned route during the process or certain stops can be omitted. In similar dynamic problems, some or all of the access points are not known in the beginning. These points are dynamically defined during the route design or planning stages. In the dynamic VRP, using a real- time communication network between the vehicle and decision-making system, the vehicle routes can be re-defined during the operation. This type of problems is defined as online or real-time problems by some scholars (Pillac, Gendreau, Guéret & Medaglia, 2013). Two examples of this can be certain orders getting cancelled or new orders being taken while a water distribution vehicle is on its route, or a school bus being informed on its route that certain students will be absent from school that day. In current conditions, dynamic VRP’s are more frequently needed, and are attributed with a more specific importance. The first study dealing with dynamic VRP was carried out by Wilson and Colvin (Pillac, Gendreau, Guéret & Medaglia, 2013). The enhancements in GPS, traffic sensors, and mobile communication systems caused a further acceleration in studies carried out in this field. Within the context of this study, DSBRP will be investigated. DSBRP is graphically explained in Figure 2.</p>
Empirical Dataset of 1200 hours of Experiment: Reliability and Performance Measurements of Dynamic Routing Applications
<p>This artifact presents the empirical measurements of an experiment with 1200 hours of runtime. We repeated the experiment 200 runs, each for 6 hours. The data folder provides the empirical data, and the plots folder gives the distribution of reliability and performance measurements in 200 runs. </p> <p><strong>For reliability</strong>, we simulate component crashes and measure the request loss. To simulate component crashes, we generate a random number for each service and router and stop the docker container if the generated number is below a crash probability (0.5% in this study). </p> <p><strong>For performance</strong>, we consider the round-trip time of requests going through several services until the requests are processed.</p> <p>The experiment cases are: </p> <p>Four levels of call frequency <span class="math-tex">\(cf\)</span>: 10, 25, 50, 100 requests/second (r/s)</p> <p>Three levels of service: 3, 5, 10<br> the number of services <span class="math-tex">\(n_{serv}\)</span> in a call sequence</p> <p>Three architectures: <br> Central Entity (CE): one router <span class="math-tex">\(n_{rout} = 1\)</span><br> Dynamic Routers (DR): three routers <span class="math-tex">\(n_{rout} = 3\)</span><br> Sidecar Architecture (SA): 1 router per service <span class="math-tex">\(n_{rout} = n_{serv}\)</span></p>
Data from: Information use by humans during dynamic route choice in virtual crowd evacuations
We conducted a computer-based experiment with over 450 human participants and used a Bayesian model selection approach to explore dynamic exit route choice mechanisms of individuals in simulated crowd evacuations. In contrast to previous work, we explicitly explore the use of time-dependent and time-independent information in decision-making. Our findings suggest that participants tended to base their exit choices on time-dependent information, such as differences in queue lengths and queue speeds at exits rather than on time-independent information, such as differences in exit widths or exit route length. We found weak support for similar decision-making mechanisms under a stress-inducing experimental treatment. However, under this treatment participants were less able or willing to adjust their original exit choice in the course of the evacuation. Our experiment is not a direct test of behaviour in real evacuations, but it does highlight the role different types of information and stress play in real human decision-making in a virtual environment. Our findings may be useful in identifying topics for future study on real human crowd movements or for developing more realistic agent-based simulations.
Data of the Paper: Analytical Modeling and Empirical Validation of Performability of Service- and Cloud-Based Dynamic Routing Architecture Patterns
<p>The online artifacts for the following article accepted at 30th Asia-Pacific Software Engineering Conference (APSEC 2023): </p><p>"Analytical Modeling and Empirical Validation of Performability of Service- and Cloud-Based Dynamic Routing Architecture Patterns"</p><p>Abstract:</p><p>Many dynamic routing architectural patterns are available, including distributed routing, e.g., using the sidecar pattern, or centralized routing, e.g., using event stores or service buses. Different Quality-of-Service (QoS) factors influence routing schemas and technology selection, such as performance, reliability, scalability, and control properties offered by the patterns. An analytical model can formalize the QoS factors and facilitate the architectural decision-making when changing the routing scheme, i.e., to more distributed or centralized routing. So far, the impact of these architectural patterns on performability, i.e., the overall performance of a system with impeded reliability, has not been extensively studied. This is important because deciding to increase performance, e.g., by parallel processing of requests, may lead to decreased reliability because of the added points of a crash. We propose an analytical performability model during component crashes. For the empirical validation of our proposed model, we ran an extensive experiment of 2412 hours of runtime on a private cloud infrastructure and Google Cloud Platform. The low prediction error of 1.75\% indicates the high accuracy of our performability model. These results provide important insights when making architectural decisions regarding service- and cloud-based dynamic routing.</p>
Data from: Information use by humans during dynamic route choice in virtual crowd evacuations
Open the record for dataset details and reuse information.
Cellular population dynamics shape the route to human pluripotency
GEO Series GSE221739. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing.
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