Search

Increasing container traffic and land acquisition problem for terminal expansion leads to usage of external yard in a port buffer area. This condition influenced the terminal performance because a road which connects the terminal and the external yard was also used by non-container traffic. Location choice problem considered to solve this condition, but the previous research has not taken account a stochastic condition of container arrival rate and service time yet. Bi-level programming framework was used to find optimum location configuration. In the lower-level, there was a problem to construct the equation, which correlated the terminal operation and the road due to different time cycle equilibrium. Container moves from the quay to a terminal gate in a daily unit of time, meanwhile, it moves from the terminal gate to the external yard through the road in a minute unit of time. If the equation formulated in hourly unit equilibrium, it cannot catch up the container movement characteristics in the terminal. Meanwhile, if the equation formulated in daily unit equilibrium, it cannot catch up the road traffic movement characteristics in the road. This problem can be addressed using simulation model. Discrete Event Simulation Model was used to simulate import container flow processes in the container terminal and external yard. Optimum location configuration in the upper-level was the combinatorial problem, which was solved by Full Enumeration approach. The objective function of the external yard location model was to minimize user transport cost (or time) and to maximize operator benefit. Numerical experiment was run for the scenario assumption of two container handling ways, three external yards, and thirty-day simulation periods. Jakarta International Container Terminal (JICT) container characteristics data was referred for the simulation. Based on five runs which were 5, 10, 15, 20, and 30 repetitions, operation one of three available external yards (external yard – 3) was the optimum result. Apparently, the model confirmed the hypothesis that there was an optimum configuration of the external yard. Nevertheless, the model needs detail elaboration related to the objective function and the optimization constraint. It requires detail validation, in term of service time value, distribution pattern, and arrival rate in each unit server modeled in the next step of the research. The model gave unique and relatively consistent value of each run. It was indicated that the method has a chance to solve the research problem.

The natural outstanding of location model is as an excellent tool for mixed variables classification among other existing approaches such as Kernel-based non-parametric classification, logistic discrimination and linear discriminant analysis. However, the presence of outliers will affect the estimation of population parameters, hence causing inability of the model to provide an adequate statistical model and interpretation as well. In other words, outliers can distort not only parameters estimation, but also lead to poor classification performance. Therefore, this article aims to develop a new framework of location model through the integration of robust technique and classical location model with mixed variables in the presence of outliers, purposely for robust classification. The developed framework produces a new location model that can be used as an alternative approach for classification tasks as for academicians and practitioners in future applications, especially when they are facing with outliers ...

Smoothed location model (SLM) is one of the discriminant analysis that can be used to deal with mixtures of continuous and binary variables simultaneously. However, SLM facing the problem in estimating parameters when the there is a large number of binary variables considered in the study. Thus, two variable extraction techniques, principal component analysis (PCA) and multiple correspondence analysis (MCA) are conducted together with SLM in order to solve the problems of many empty cells and parameters estimation. Simulation results showed that SLM along with PCA+Adjusted MCA performed better than SLM with PCA+ Indicator MCA even when the number of extracted binary is large.

With regard to the advancement of global information and communication technology (ICT), Telecenter today has contributes actively in the rural community development whereas location is a major consideration for it sustainability. The Telecenters denoted the possibility of achieving four key performances namely, the sustained will, participation, commitment and cooperation of the local public. This study is aimed to construct a spatial location model for understanding the sustainability of Telecenter by using hybrid method of spatial information systems approach and survey data. The suggestion and implications of this study are briefly explore where Telecentres acting as catalysts to widespread digital opportunities there is every possibility for futher ICT adaptation, diffusion and innovation among the rural dwellers of Malaysia.

Smoothed location model is a discriminant analysis which can be used to handle the data involving mixtures of continuous and binary variables simultaneously. This model is introduced to handle the problem of some empty cells due to the increasing of binary variables. However, smoothed location model is infeasible if involve large number of binary variables. Therefore, the combination of two variable extraction approaches, principal component analysis and multiple correspondence analysis are carried out before the construction of smoothed location model in order to extract large number of measured variables in the study. In fact, there are four types of multiple correspondence analysis but only Burt matrix multiple correspondence analysis had been applied in the latest investigation. Thus, this study aims to examine and compare principal component analysis with four types of multiple correspondence analysis and hope to have better results for data with large number of mixed variables. The proposed model is expected to provide a better or at least comparable classification performance as comparing to others classification methods.

The natural performance of the location model is a potential tool for allocating an object into one of the two observed groups involving mixtures of continuous and binary variables. In constructing location model, continuous variable is used to estimate parameters while binary variable is utilized to create segmentation in each group. Such segmentation is called as multinomial cells. Basically, the multinomial cells will grow exponentially according to the number of the binary variable. These multinomial cells will become empty when there is no object can be assigned into some of them. Then the occurring of empty cells will lead to unreliable parameter estimation. Consequently, the construction of the discriminant rule based on location model is impossible. Therefore, this paper attempts to discuss how the location model based on maximum likelihood estimation can be constructed even dealing with many measured binary variables. In other word, how is location model able to deal with the issue of many empty ...

Student's orientation from primary to the secondary schools is a vital process and need to be considered as yearly problem by The District Education Office of Ministry of Education Malaysia. The allocation of students to the right schools becomes complicated due to several constraints like capacity of seats in secondary schools, widespread demand areas as well as preferences by the parents. Considering all the constraints, this study proposes the application of location model to allocate students the right school. The allocation of student from primary to secondary school is based on total number of students, the availability of seats from primary and at the secondary schools, and distance from student's home to the nearest schools, both primary and secondary schools. The problem is modelled as hierarchical P-median problem as there are two hierarchical levels in this orientation. An alternative Genetic Algorithm (GA) based heuristic is applied to solve the problem and results are compared with Excel Solver.