This paper proposes a vehicle travel speed model to enhance two heuristic algoritihms from previous studies, namely current initial solution (CIS) and different initial customer (DIC). Both algorithms are used to solve a real-life waste collection vehicle routing benchmark problem with dynamic travel speeds. This problem is referred to as Time-Dependent Vehicle Routing Problem (TD-VRP) in previous literature. The benchmark problem consisted of ten sub problems, involving up to 2092 customers. Previous studies solved the benchmark problem using DIC and CIS algorithms with the assumption that the vehicles are travelling with a static speed when collecting the waste. However, in this paper the static speed that was considered in both algorithms were improved by introducing dynamic travel speeds to construct vehicle routes for the waste collection drivers. Compared to previous studies the enhanced CIS and DIC with dynamic travel speeds affected the waste collection problem in terms of the number of vehicles used, the total distance travelled and the total travel time. However, different settings of speed may give different impacts to the solution. The study reveals that with a setting of dynamic speed between 40 mph and 55 mph, DIC is able to reduce two vehicles (from 98 to 96 number of vehicles used), 7.85% of total distance travelled, and 19.10% of total travel time.
Large volume of educational data has led to more challenging in predicting student's performance. In Malaysia currently, study about the performance of students in Malaysia institutions is very little being addressed. The previous studies are still insufficient to identify what factors contribute to student's achievements and lack of investigations on exploring pattern of student's behaviour that affecting their academic performance within Malaysia context. Therefore, predicting student's academic performance by using decision trees is proposed to improve student's achievements more effectively. The main objective of this paper is to provide an overview on predicting student's academic performance using by using data mining techniques. This paper also focuses on identifying the pattern of student's behaviour and the most important attributes that impact to the student's achievement. By using educational data mining techniques, the students, lecturers and academic institution are able to have a better understanding on the student's achievement.
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