Solving School Bus Routing Problem using Genetic Algorithm-based Model

Abstract

School Bus Routing Problem is an optimization problem which falls under the class of the Vehicle Routing Problem. It involves the use of a fleet of vehicles to efficiently and optimally transport students to and from their schools. To solve this problem, optimal school bus routes are found by minimizing the number of buses, the number of routes and the total distance traversed along all routes. Manual routing of school buses have led to creation of many routes, increased number of buses and several buses navigating the same route, thereby incurring more cost. One of such methods used in solving school bus routing problems is meta-heuristic method which has proven better results in terms of optimal solution and reduced time complexity. In this study, Genetic algorithm is utilized to solve the school bus routing problem because of its simplicity and ability to generate many possible solutions. The algorithm is implemented in C# programming language and tested using secondary data obtained from Ondo State Free-School Bus Shuttle Scheme, Akure, Nigeria. The result shows that of all four nodes (bus stops) used in performance evaluation, Alakure to Oke-Aro junction bus stop presents as the best route which covers a total of 69 nodes with a total distance of 34.5km. This shows that there can be less number of buses in use and reduced number of routes in which the buses are assigned.

Employee Shuttle Bus Routing Problem

“…In the literature, SBRP has been categorized under different sub-groups depending on the considered problem: bus stop selection, bus route generation, and bus route scheduling, etc. Among these problems, bus stop selection problem [5,6,7] identifies a bus stop location for each student, bus route generation problem [8,9,10,11,12] determines the visiting sequences of predetermined bus stops, and bus route scheduling problem [13,14] includes a time dimension to determine exact loading time of the students. The main focus of this work will be on the route generation part since the company has already fixed the possible pickup locations (bus stops), and the time of loading at each stop is not their primary focus, which is post adjustable.…”

A Visual Analytics Approach to Scheduling Customized Shuttle Buses via Perceiving Passengers' Travel Demands

“…Understanding Human Mobility Pattern has significant impacts and enables various applications such as urban planning [35], region function analysis [31,32], hotspots detection [36], driving route recommendation [11,33], and bus route planning [4,5,21,24]. Bastnai et al [4] grouped taxi trips into clusters and identified a route connecting multiple clusters to maximize the sum of each connected trip cluster without considering other constraints such as time.…”

Ruteo de buses escolares con consideraciones ambientales mediante Búsqueda Tabú Granular

“…El SBRP se encuentra clasificado en la literatura como un problema de optimización combinatoria [5], el cual busca diseñar rutas para una flota de buses escolares con el objetivo de minimizar el tiempo y los costos [6] al prestar el servicio de transporte de estudiantes hacia sus respectivas escuelas o colegios en áreas rurales o urbanas.…”

“…Por el lado de las meta heurísticas se encuentran investigaciones en ruteo de buses escolares que emplean búsqueda tabú [14], algoritmos genéticos [5], optimización por enjambre de partículas [18], colonia de hormigas [19], la meta heurística GRASP [20] entre otras. Sim embargo, no se encuentran investigaciones que empleen la búsqueda tabú granular en el problema del rute de buses escolares, a pesar de que ha gozado de gran aceptación en problema de ruteo de vehículos de carga [21].…”

“…La meta heurística puede aplicar como criterio de aspiración el hecho de que si el valor de la función objetivo de la nueva solución es menor 2 ( ) < 1 ( ), el movimiento que produce a S es ejecutado inclusive si este es un movimiento tabú. También, las estrategias de diversificación e intensificación se vinculan a los valores de dispersión , de manera que tomará valores grandes para encontrar soluciones no visitadas antes (inducir procesos de diversificación) y tomará valores pequeños para encontrar soluciones cercanas a la mejor solución encontrada (permitir procesos de intensificación) [5].…”