Sensitivity Analysis Model Bus Timetabling Problem by Considering Passenger Ease
Hagni Wijayanti1, Fajar Delli Wihartiko2, Fitria Virgantari3
1Hagni Wijayanti, Department of Mathematic, Universitas Pakuan, Indonesia.
2Fajar Delli Wihartiko, Department of Computer Science, Universitas Pakuan, Indonesia.
3Fitria Virgantari, Department of Mathematic, Universitas Pakuan, Indonesia.
Manuscript received on 02 August 2019 | Revised Manuscript received on 25 August 2019 | Manuscript Published on 05 September 2019 | PP: 11-16 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10030782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1003.0782S719
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Bus Timetabling Problem, Sensitivity Analysis, Optimization.
Scope of the Article: Predictive Analysis