Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

Wihartiko, Fajar Delli; Wijayanti, Hagni; Virgantari, Fitria

doi:10.1088/1757-899x/332/1/012020

Cited by 36 publications

(18 citation statements)

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“…The experimental results use different data, have different performance times according to the number of data input. The time performance generated for processing in 3 cases of data according to the study [15] can be seen in Table 5. From the table it appears that relatively long time is needed to work on the problem of big-scale outlier detection.…”

Section: Results and Analysismentioning

confidence: 99%

Fraud Detection of Bus Ticket Sales by using Spatio Temporal Data Mining

2019

ijrte

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Cheating in the sale of bus tickets is often found in transportation service providers who still use the conductor as a ticket seller on the bus. The high cost of supervision, lack of honesty, unification of the sales function and the ticket control function on the conductor makes this fraudulent practice a problem that companies must handle. By looking at the behavior of ticket sales for each individual through the method of spatio temporal clustering can detect fraudulent behavior that occurs. The bus ticket sales deception process is implemented in Bogor's Bus Rapid Transit (BRT). The results show that there are 3.2% of high-potential officers cheating ticket sales. By knowing the cheating behavior of ticket sales, the company can follow up with the policy so that cheating behavior does not become the company culture

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Section: Results and Analysismentioning

confidence: 99%

Fraud Detection of Bus Ticket Sales by using Spatio Temporal Data Mining

2019

ijrte

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“…It is difficult to determine the dynamic response in between two dynamic friction coefficients without the use of metaheuristic methods such as genetic algorithm and PSO. ere is no significant difference between these two methods in small scale; however, differences are seen in medium and large scale where genetic algorithms can only produce feasible solutions that are nearly optimal, while the PSO algorithm has ease of implementation and also has high calculation accuracy [44]. erefore, system identification using the PSO method was proposed for model parameter identification of the extracted hysteresis model.…”

Section: Model Parameter Identificationmentioning

confidence: 99%

Inverse Modeling of Nonlinear Artificial Muscle Using Polynomial Parameterization and Particle Swarm Optimization

Dzahir

Yamamoto

2020

Advances in Materials Science and Engineering

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The properties of pneumatic artificial muscle (PAM) with excellent power-to-weight ratio and natural compliance made it useful for healthcare engineering applications. However, it has undesirable hysteresis effect in controlling a robotic manipulator. This behavior is quasistatic and quasirate dependent which changed with excitation frequency and external force. Apart from this, it also inherits frictional presliding behavior with nonlocal memory effect. These nonlinearities need to be compensated to achieve optimal performance of the control system. Even though an inverse modeling of PAM has limited application, it is important on certain control system implementation that requires the solution to the inverse problem. In this paper, the inverse modeling of PAM in the form of activation pressure was proposed. This activation pressure model was derived according to static pressure and extracted hysteresis components from pressure/length hysteresis. The derivation of the static pressure model follows the phenomenological-based model of third-order polynomial. It is capable of characterizing the nonlinear region of PAM at low and high pressure. The derivation of extracted hysteresis model follows the mechanism of dynamic friction. In this principle, the activation pressure model was dependent on regression coefficient of the static pressure model and dynamic friction coefficients of the extracted hysteresis model. The regression constants of these coefficients were characterized from the hysteresis dataset by using model parameter identification and the particle swarm optimization (PSO) method. The result from model simulation shows the root mean square error (RMSE) value of less than 10% error was evaluated at various excitation frequencies and external forces. This inverse modeling of PAM implemented a simple approach, but it should be useful in control design applications such as rehabilitation robotics, biomedical system, and humanoid robots.

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“…The timetable Model (1) cannot be solved directly using the branch and bound algorithm because the constraint (1.6) is a conditional constraint in which a value of Nevertheless, the genetic algorithm is only capable of providing a solution that is near optimal [1]. On one side of the study [11] shows that the branch and bound algorithm still shows the best performance for the case of bus time tabling problem.…”

Section: B Problem Solutionmentioning

confidence: 99%

“…Constraint (10) assures that the resulting solution is an integer of 1 to the number of set members of that number (|Y|). Model Eqs (7)- (11) have not produced a bus departure time table. The bus departure schedule of the optimal number of trips / headways can be made based on the following algorithm:…”

Section: B Problem Solutionmentioning

confidence: 99%

Sensitivity Analysis Model Bus Timetabling Problem by Considering Passenger Ease

2019

ijrte

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The optimal Model Bus Timetabling Problem (MBTP) is a simplification of bus scheduling cases where a bus departure schedule with the optimal number of trips will be searched. The optimal number of trips is defined by maximizing the profit function of the bus by considering constraints such as the availability of buses, the executing officers, the number of passengers and the travel time. This model was developed by adding the constraints of ease of passengers in remembering the schedule. The model is then modified and completed using the branch and bound algorithm. The result of the sensitivity analysis to the model shows that the addition of bus or employee by the manager will not be directly proportional to the addition of passengers. So the addition of buses and employees is done when passengers increase. In addition, the addition of constraints ease passengers in reading the time table will make the increase in corporate profits with the consequences of decreased levels of passenger comfort. If the company chooses to improve the comfort of passengers it will affect the increase of the company's operational costs.

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Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

Cited by 36 publications

References 0 publications

Fraud Detection of Bus Ticket Sales by using Spatio Temporal Data Mining

Fraud Detection of Bus Ticket Sales by using Spatio Temporal Data Mining

Inverse Modeling of Nonlinear Artificial Muscle Using Polynomial Parameterization and Particle Swarm Optimization

Sensitivity Analysis Model Bus Timetabling Problem by Considering Passenger Ease

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