Zaitul Marlizawati Zainuddin

A perturbation-based heuristic for the capacitated multisource Weber problem

This paper proposes a perturbation-based heuristic for the capacitated multisource Weber problem. This procedure is based on an effective use of borderline customers. Several implementations are considered and the two most appropriate are then computationally enhanced by using a reduced neighbourhood when solving the transportation problem. Computational results are presented using data sets from the literature, originally used for the uncapacitated case, with encouraging results.

Spectral Estimation of Nonstationary EEG Using Particle Filtering With Application to Event-Related Desynchronization (ERD)

This paper proposes non-Gaussian models for parametric spectral estimation with application to event-related desynchronization (ERD) estimation of nonstationary EEG. Existing approaches for time-varying spectral estimation use time-varying autoregressive (TVAR) state-space models with Gaussian state noise. The parameter estimation is solved by a conventional Kalman filtering. This study uses non-Gaussian state noise to model autoregressive (AR) parameter variation with estimation by a Monte Carlo particle filter (PF). Use of non-Gaussian noise such as heavy-tailed distribution is motivated by its ability to track abrupt and smooth AR parameter changes, which are inadequately modeled by Gaussian models. Thus, more accurate spectral estimates and better ERD tracking can be obtained. This study further proposes a non-Gaussian state space formulation of time-varying autoregressive moving average (TVARMA) models to improve the spectral estimation. Simulation on TVAR process with abrupt parameter variation shows superior tracking performance of non-Gaussian models. Evaluation on motor-imagery EEG data shows that the non-Gaussian models provide more accurate detection of abrupt changes in alpha rhythm ERD. Among the proposed non-Gaussian models, TVARMA shows better spectral representations while maintaining reasonable good ERD tracking performance.

Routing Protocols in Vehicular Ad hoc Networks: Survey and Research Challenges

A Vehicular Ad hoc Network (VANET) is a type of wireless ad hoc network that facilitates ubiquitous connectivity between vehicles in the absence of fixed infrastructure. Multi-hop routing and beaconing approaches are two important research challenges in high mobility vehicular networks. Routing protocols are divided into two categories of topology-based and position-based routing protocols. In this article, we perform a comparative study among the existing routing solutions, which explores the main advantages and drawbacks behind their design. After implementing the representatives of geographical and topology routing protocols, we analyze the simulation results and discuss the strengths and weaknesses of these routing protocols in regard to their suitability to vehicular networks. Lastly, we discuss the open issues and research directions related to VANET routing protocols.

Modeling and estimation of single-trial event-related potentials using partially observed diffusion processes

This paper proposes a new modeling framework for estimating single-trial event-related potentials (ERPs). Existing studies based on state-space approach use discrete-time random-walk models. We propose to use continuous-time partially observed diffusion process which is more natural and appropriate to describe the continuous dynamics underlying ERPs, discretely observed in noise as single-trials. Moreover, the flexibility of the continuous-time model being specified and analyzed independently of observation intervals, enables a more efficient handling of irregularly or variably sampled ERPs than its discrete-time counterpart which is fixed to a particular interval. We consider the Ornstein-Uhlenbeck (OU) process for the inter-trial parameter dynamics and further propose a nonlinear process of Cox, Ingersoll & Ross (CIR) with a heavy-tailed density to better capture the abrupt changes. We also incorporate a single-trial trend component using the mean-reversion variant, and a stochastic volatility noise process. The proposed method is applied to analysis of auditory brainstem responses (ABRs). Simulation shows that both diffusions give satisfactory tracking performance, particularly of the abrupt ERP parameter variations by the CIR process. Evaluation on real ABR data across different subjects, stimulus intensities and hearing conditions demonstrates the superiority of our method in extracting the latent single-trial dynamics with significantly improved SNR, and in detecting the wave V which is critical for diagnosis of hearing loss. Estimation results on data with variable sampling frequencies and missing single-trials show that the continuous-time diffusion model can capture more accurately the inter-trial dynamics between varying observation intervals, compared to the discrete-time model. Continuous-time partially observed diffusion modeling of single-trial ERPs.Improved modeling of hidden continuous dynamics and irregularly spaced ERPs.Ornstein-Uhlenbeck and Cox, Ingersoll & Ross process (to capture abrupt changes).Incorporate single-trial trend component and stochastic volatility noise process.Better single-trial estimates of auditory brainstem responses & wave V detection.

Artifact Removal from Single-Trial ERPs using Non-Gaussian Stochastic Volatility Models and Particle Filter

This paper considers improved modeling of artifactual noise for denoising of single-trial event-related potentials (ERPs) by state-space approach. Instead of the inadequate constant variance models used in existing studies, we propose to use stochastic volatility (SV) models to better describe the time-varying volatility in real ERP noise sources. We further propose a class of non-Gaussian SV models to capture the abrupt volatility changes typically present in impulsive noise, to improve artifact removal from ERPs. Two specifications are considered: (1) volatility driven by a heavy-tailed component and (2) transformation of volatility. Both result in volatility processes with heavy-tailed transition densities which can predict the impulsive noise volatility dynamics, more accurately than the Gaussian models. These SV noise models are incorporated in an autoregressive (AR) state-space ERP dynamic model. Parameter estimation is done using a Rao-Blackwellized particle filter (RBPF). Evaluation on simulated auditory brainstem responses (ABRs), corrupted by real eye-blink artifacts, shows that the non-Gaussian models can accurately detect the artifact-induced abrupt volatility spikes, and able to uncover the underlying inter-trial dynamics. Among them, the log-SV model performs the best. The results on real data demonstrate significant artifact suppression.

Development of integrated model for continuous berth allocation problem and quay crane scheduling with non crossing constraint

High efficiency of port operation is required to succeed in the competition between port container terminals. Berth Allocation and Quay Crane Scheduling are the most important part in container terminal operations. The integrated model is formulated as a MIP problem with the objective to minimize the sum of the dwell times, where a vessels dwell time is measured between arrival and departure including both times waiting to be berthed and servicing time while berthed. The construction of suitable mathematical model is formulated by considering various practical constraints.

MILP model for integrated balancing and sequencing mixed-model two-sided assembly line with variable launching interval and assignment restrictions

This research explores the Mixed-Model Two-Sided Assembly Line (MMTSAL). There are two interrelated problems in MMTSAL which are line balancing and model sequencing. In previous studies, many researchers considered these problems separately and only few studied them simultaneously for one-sided line. However in this study, these two problems are solved simultaneously to obtain more efficient solution. The Mixed Integer Linear Programming (MILP) model with objectives of minimizing total utility work and idle time is generated by considering variable launching interval and assignment restriction constraint. The problem is analysed using small-size test cases to validate the integrated model. Throughout this paper, numerical experiment was conducted by using General Algebraic Modelling System (GAMS) with the solver CPLEX. Experimental results indicate that integrating the problems of model sequencing and line balancing help to minimise the proposed objectives function.

A simulated annealing approach for redesigning a warehouse network problem

Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.

A Simultaneous Integrated Model with Multiobjective for Continuous Berth Allocation and Quay Crane Scheduling Problem

This paper presents the simultaneous integration model of berth allocation and quay crane scheduling. Berths and quay cranes are both critical resources in port container terminals. The mathematical model uses a mixed integer linear programming with multiple objectives generated by considering various practical constraints. Small data instances have been taken to validate the integrated model. A numerical experiment was conducted by using LINGO programming software to evaluate the performance and to obtain the exact solution of the suggested model.

The Determining Factors in Prescribing Anti-Hypertensive Drugs to First-Ever Ischemic Stroke Patients

Prescription of drugs plays a vital role not only in patient care but also to the health care industry for the many consequences it is entangled with. With the advanced use of mathematical models in healthcare today, it has become possible for healthcare personnel to practice evidence-based- decision making. This paper attempts to aid in the prescription of anti-hypertensive drugs for first ever ischemic stroke patients by conducting sensitivity and post-optimality analysis of a previously proposed multi-objective mixed-integer nonlinear integer programming model. Determining factors that contribute to the optimal drug prescription will be identified, enabling decision makers to make informed decisions on how a patients drug therapy needs to be adjusted when the need arises.