Engineering education has a crucial role in the economic and social development of society; this paper aims to investigate the most significant factors that contributing in improving the quality of the engineering education. Furthermore, identify the factors affecting the students’ academic performance. The study was conducted by using statistical Design of Experiments (DOE) approach to investigate the main effects of lecture delivery, assessment, engagement, communication, and interaction with instructors. Minitab software is implemented to perform statistical analysis to identify the significance of each factor, and degree by which each factor influences the students’ academic achievement. Results showed that the students’ academic performance is affected by the availability of course materials, and communications with instructors.

This paper addresses a bi-criteria optimization problem to minimize total flow time and makespan simultaneously for a cellular flowshop with Sequence Dependent Setup Times (FMCSP with SDSTs); A multi-objective Particle Swarm Optimization (MPSO) and a Multi-objective Simulated Annealing (MOSA) Algorithm are proposed to solve the proposed problem. furthermore, an improved algorithm (named as IMPSO-TA), where MPSO is combined with Threshold Acceptance (TA) algorithm to improve the convergence of the obtained Pareto Fronts. The proposed algorithms are evaluated using several Quality Indicators (QI) measures for multi-objective optimization problems. Results showed that proposed algorithms can generate approximated Pareto Fronts in a reasonable CPU time. Furthermore, quality of Pareto fronts generated by IMPSO-TA is better than Pareto fronts found by MPSO and MOSA based on the test problems that are used in this research at the cost of CPU time. Further, the proposed IMPSO-TA performs as best available algorithms in the literature for small and medium test problems with a very minor deviation for best results for large test problems.

Abstract

Walking considered as one of the safest modes of

travel available, sustainable to human society as well as

environmentally beneficial. In this context, the aim of this

research is to investigate pedestrians’ traffic gap acceptance

and crossing decision for Mid-block Street crossing in urban

areas in Malaysia. Pedestrian crossing behaviour at

Rughaya Street has been examined in terms of the decision

to cross or not the street and size of traffic gaps accepted by

pedestrian, as well as the related contributing factors. A

field study was conducted to collect the data of pedestrians’

decisions under real mix traffic Condition using video

camera on a typical unsignalized urban street section. JPEG

files were obtained from video recording by using Snapshot

Wizard software. The data extracted included traffic

characteristics such as traffic size, traffic speed, etc.

pedestrian individual characteristics such as gender, in

addition to individual behavior such as waiting, frequency

of attempt, etc.). Furthermore, The extracted data were

used to develop and examine a pedestrian gap acceptance

model based on A lognormal regression model and binary

logistic model by SPSS (22) in order to validate the impact

of various parameters on the size of traffic gaps accepted by

pedestrians as well as the effect on the decision of

pedestrians to cross the street or not. So that the effect of

the gap accepted available and of other factors on the

decision of pedestrians to cross the street or not is examined.

These results indicate that the data set for this particular

location has a majority of male pedestrians which were

insignificant variables in both models moreover a lognormal

regression results shows that accepted gaps size depend on

traffic size, crossing distance, speed of approaching vehicle

and time spent by pedestrian at the curb waiting for a

suitable gap size to start crossing. The BL model performs

well for the reason that it captures the pedestrian decision

making process with traffic taking the relevant attributes

into consideration. According to the coefficients of BL

regression analysis equation we noticed that the illegal

parking, traffic size, traffic waiting time and gap size are the

vital attributes for the Pedestrian gap acceptance model..

Index Terms: Pedestrian crossing, gap acceptance, crossing

decision, multiple linear regression, binary logistic

regression.