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Public urban transport optimization by means of tabu search and pso algorithms: Medellín, colombia

Abstract:

Urban public transport in the city of Medellín (Col) has had a positive development, however insufficient due to the increase in population density. This paper presents a comparative analysis of the Tabu Search algorithm (TS) and the Particle Swarm Optimization algorithm (PSO). It proposes an optimization of the urban public transport service in the northern area of the city, using variables from different organizational units (vehicle mechanics, human resources management, environmental and operational management). The algorithms achieved convergence with the objective of maximizing profitability regarding the use of buses during the operating day. A route planning proposal was obtained that allows a user’s increment of 25%, improve service times, generating sustainable development for the environment and the transport company.