Abstract
In this paper, a two-step meta-heuristic approach is proposed for vehicle assignment problem with geometric shape-based clustering and genetic algorithm. First, the geometric shape-based clustering method is used and then the solution of this method is given to the genetic algorithm as initial solution. The solution process is continued by genetic algorithm. There are 282 bus lines in İstanbul European side. Those buses should be assigned to six bus garages. The proposed method is used to determine the minimum distance between the bus lines and garages by assigning buses to garages. According to the computational results, the proposed algorithm has better clustering performance in terms of the distance from each bus-line start point to each bus garage in the cluster. The crossover rate changing method is also applied as a trial in order to improve the algorithm performance. Finally, the outputs that are generated by different crossover rates are compared with the results of the k-Nearest Neighbour algorithm to prove the effectiveness of the study.
Highlights
Migration of rural population to urban areas is still continuing globally
In the first step clustering is performed by geometric shape-based approach and the results of this step are used for an initial solution of the genetic algorithm
This is the first study which proposes a twostep meta-heuristic approach for the assignment problems based on a geometric shape-based clustering (GSBc) and genetic algorithm (GA)
Summary
Especially in the developing countries cannot cope with the pace of population growth. This creates major problems that affect the city life quality. The increasing role of public transport has created a scientific research area of “total travel time optimization”. In this sense, minimization of the total travel time (transfer-waiting-in vehicle included) is a major focus area. Minimization of the total travel time (transfer-waiting-in vehicle included) is a major focus area Both customers of public transport (shortened travel time), other residents (less traffic), and city economy (efficient use of funds) benefit from this optimization
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