Improvement of Public Cab Transportation System through Computer Simulation
Asian Journal of Economics, Business and Accounting,
Aims: Public transportation is a dynamic problem which depends of different variables such as service schedules, route times, demand rates, and traffic density. Due to these features, mathematical modelling for improvement purposes is a difficult and complex task. Thus, computer simulation is proposed as a more comprehensive and efficient approach.
Study Design: A transportation case study is considered with the main feature variables. Data was obtained from a cab system within a town in Mexico. The reported problems consisted of economic losses due to low service level and cab utilization,
Methodology: The transportation problem consists of a cab system with four main routes which are served twice a day. Data associated to route times, demand rates, cab capacities, fleet size, service restrictions and fare costs was collected to develop a simulation model and analyze the current state of the real system. Once validated, an improvement approach was performed on the simulation model.
Results: The validated simulation model corroborated the problems reported by the case study. The proposals to improve performance, which consisted of reduction of cab fleet and route length led to reduce operative costs without affecting service level.
Conclusion: Simulation is an important tool to improve complex logistic systems which cannot be addressed by standard mathematical modelling.
- Discrete-event simulation
- transportation system
- business analytics
- operative costs
How to Cite
Obaidat MS, Nicopolitidis P, Zarai F. Modeling and Simulation of Computer Networks and Systems: Morgan Kaufmann Ed.; 2015.
Schriber T, Brunner DT, Smith JS. Inside discrete-event simulation software: How it works and why it matters. In Proc. of the 2013 IEEE Winter Simulations Conference (WSC). 2014;8-11 December 2013, Washington D.C., USA.
Manuj I, Mentzer J, Bowers M. Improving the rigor of discrete‐event simulation in logistics and supply chain research. International Journal of Physical Distribution & Logistics Management. 2009;39(3):172-201.
Sargent, R. Verification and Validation of Simulation Models. Journal of Simulation. 2010;7:12-24.
Kamrani M, Abadi SMH, Golroudbary SR. Traffic simulation of two adjacent unsignalized T-junctions during rush hours using Arena software. Simulation Modelling Practice and Theory. 2014;49:167-179.
Zahraee S, Esrafilian R, Kardan R, Shiwakoti N, Stasinopoulos P. Lean construction analysis of concrete pouring process using value stream mapping and Arena based simulation model. Materials Today. 2021;42(2):1279-1286.
Guseva E, Varfolomeyeva T, Efimova I, Movchan I. Discrete event simulation modelling of patient service management with Arena. Journal of Physics: Conference Series. 2018;1015(3):1-7.
Kelton W, Sadowski R, Zupick N. Simulation with Arena. 6th ed. United States: SEM; 2014.
Osaba E, Onieva E, Carballedo R, Diaz F, Perallos A. An Adaptive Multi-Crossover Population Algorithm for Solving Routing Problems. In Nature Inspired Cooperative Strategies for Optimization, Terrazas et al. (Ed.). Springer International: Publishing Switzerland. 2014;113-124.
Brailsford SC, Eldabi T, Kunc M, Mustafee N, Osorio AF. Hybrid simulation modelling in operational research: A state-of-the-art review. European Journal of Operational Research. 2019;278(3):721-737.
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