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OPTIMIZATION OF SCHOOL BUSES ROUTING PROBLEM K242/41

(SBRP) USING GENETIC ALGORITHM APPROACH

ILLI SYAFIQAH BINTI ABDUL RAZAK SUPERSISOR:

2023680518 PUAN WAN NURFAHIZUL IFWAH BT WAN ALIAS

This study presents an optimization approach for the School Bus Routing Problem (SBRP) using the Genetic

ABSTRACT

Algorithm to minimize the total travel distance involved in student transportation. A total of 21 depot, including a fixed

depot 0 were used to simulate school bus operations where each bus must start and end at the same depot while

visiting all designated student pick-up points. The algorithm was implemented using Microsoft Excel and optimisation

was conducted 10 iterations. In each iteration, two routes which is Route X and Route Y were evaluated based on total

travel distance in kilometres. Three operators of GA crossover, mutation and inversion were applied. The shortest

route achieved was 20.634 km (Route X) in the final iteration, significantly reduced from its initial value of 62.628 km.

The results demonstrate the effectiveness of the Genetic Algorithm in solving school bus routing problems, achieving

substantial distance reduction and promoting more efficient planning in school transportation systems.

Inefficient school bus routes often result in OBJECTIVES 1.To calculate total distance of

PROBLEM excessive travel distances, fuel travel in delivering students.

STATEMENT
consumption, and operational delays, 2.To select the best routes on

negatively impacting both cost and service

quality. distance optimization by using

Genetic Algorithm.

Traditional manual planning methods

struggle to address complex routing

constraints; hence, this study adopts a

Genetic Algorithm to optimise routes and

minimise total travel distance effectively.

METHODOLOGY &

IMPLEMENTATION

RESULTS &

DISCUSSION

The algorithm evaluated two routes (X and Y)

over 10 iterations. Route X improved from

62.628 km to 20.634 km, while Route Y

reduced from 53.893 km to 29.028 km. The

optimal solution appeared in Iteration 10 with

Route X producing the shortest and most

efficient route, following the sequence 0-15-

14-16-17-18-13-12-9-10-11-8-7-2-1-4-5-6-

3-20-19-0

Future research should consider incorporating

RECOMMENDATION real-world constraints such as student pick-up

time windows, traffic patterns, bus capacity

limits, and emergency scenarios to enhance the

CONCLUSION This study successfully demonstrated the effectiveness model’s practicality and applicability.

of Genetic Algorithm (GA) in solving the School Bus

Routing Problem (SBRP). By iteratively applying

crossover, mutation and inversion, the algorithm The use of hybrid optimisation methods

produced an optimised route of 20.634 km, significantly combining Genetic Algorithm with techniques

shorter than the initial path 62.628km. The results like Ant Colony Optimisation or Tabu Search

confirm GA’s capability to minimise total travel distance, should be explored to improve convergence

improve route efficiency, and contribute to cost savings speed and solution diversity.

and better safety in school transportation systems.