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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>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 18. Drawing of the time series for males and females of primary stage students and its prediction)
<p>Note that the Tabulated value equals 3.841 while the Q value is less than Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 17. Drawing of the time series for females of primary stage students and its prediction
<p>It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 16. Drawing of the time series for males of primary stage students and its prediction
<p>It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 15. Drawing of autocorrelation function and partial correlation of the residues for males and females primary stage students
<p>After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for males and females of primary stage students, through applying the compute (Ljung-Box Q) to check the model accommodation on the Function level 0.05 so the Q value occurs of males and females of primary stage students: Ljung-Box Q' = 1.10306, With p-value = P(Chi-square(1) > 1.10306) = 0.2936 Note that the Tabulated value equals 3.841 while the Q value is less than Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 13. Drawing of autocorrelation Function and partial correlation of the residues for primary stage males students
<p>It's possible to notice the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for males primary stage, in which the residues value is located within the confidence interval limits which means the residues series is random and the Evaluated Model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm- Figure 12. Drawing of autocorrelation function and partial correlation for females primary stage students
<p>We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 11. Drawing the time series for males and females primary stage after the First difference
<p>We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 10. Drawing of autocorrelation function and partial correlation for Males and Females primary stage students
<p>The instability of the time series is recognized, and to be more accurate, we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (10).</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 14. Drawing of autocorrelation function and partial correlation of the residues for primary stage males students
<p>After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for primary stage female students, through applying the compute (Ljung- Box Q) to check the model accommodation on the function level 0.05 so the Q value occurs of primary stage female students: Ljung-Box Q' = 0.966626, With p-value = P(Chi-square(1) > 0.966626) = 0.3255 However, the Tabulated value equals 3.841 whilst the Q value is less than Tabulated value, so it accepts the Null Hypothesis which indicates the emptiness of the evaluated model out of the contrast accordance trouble. It's possible to notice the two parameters functions (Autocorrelation and Partial Correlation Functions) of the residues for females primary stage students, in which the residues value is located within the confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is shown.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 9. Drawing the time series for males and females primary stage students
<p>The Unit Radix Dickey-Fuller Test is used to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.736458 , Statistic Test = 0.380545 , P-Value = 0.794 We notice from the values above P-Value = 0.794on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis ( The Nonexistence of a Radix Unit) implies that the time series is stable. Figure (9) represents the time series of females and males in the primary stage students.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 7. Drawing the time chain for Females primary stage students after the first difference
<p>We use the Unit Radix Dickey-Fuller Test to assert the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.47242, Statistic Test = 1.22797, P-Value = 0.9445 We notice from the values above P-Value =0.9445 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, it is noticed that the stability of the time series has been accomplished. See Figure 7.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 6. Drawing of auto correlation function and partial correlation for females primary stage students
<p>The instability of the time series is noticed and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to affirm the stability according to the figure 6.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 4. Drawing of autocorrelation function and partial correlation for males primary stage students
<p>We get to notice the stability of the time series, and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure (4).</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 5. Drawing the time series for primary stage female students
<p>We use the Unit Radix Dickey-Fuller Test to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.233403, Statistic Test = 0.125769, P-Value = 0.6405 The values above P-Value = 0.6405 is noted on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis (The Nonexistence of a Radix Unit) implies that the time series is stable. Figure 5 represents the Time series of Female Primary Stage Students.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 8. Drawing of autocorrelation function and partial correlation for Females primary stage students
<p>The stability of the time series is observed and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (8).</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 3. Drawing the time series for Males primary stage students after getting the first difference
<p>The Unit Radix Dickey-Fuller Test is used to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.276151, Statistic Test = 0.87796, P-Value = 0.8984 We get to notice from the values above P-Value = 0.8984 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, it is observed that the stability of the Time Series has been accomplished . See figure 3.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 2. Drawing of autocorrelation function and partial correlation for males primary stage
<p>We get to notice the instability of the time series, and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure 2.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.