Applied Mathematics and Computational Intelligence (AMCI)

Improved Tabu Search Method in Solving Overbooking Appointment Scheduling with No-shows Patient

Nazeelda Zukernain — 2025-06-17

No-shows patient refer to instances where individuals either do not attend their scheduled appointments or cancel at the last minute, resulting in a missed opportunity for the health facility to utilize that time slot. This can lead to both time and financial losses for the facility, disrupting patient care. By having an efficient appointment schedule, these disruption can be overcome by minimizing resource idle time, resource overtime and patient waiting time. This research aims to enhances appointment scheduling by addressing overbooking through a heuristic approach, further refined by the tabu search method. The impact of scheduling multiple patients in the same time slot is examined to determine the optimal number of patients per slot for cost optimization. This problem is addressed using the C programming language. The findings indicate that the tabu search method slightly outperforms the heuristic approach, especially when dealing with larger patient datasets. Other than that, it is proven that the tabu search method functions effectively by having a long-term memory as it executes the programmer faster than previous methods such as genetic algorithm and simulated annealing. Besides, tabu search method is capable in improving the maximum number of patients that can be effectively assigned to the same time slot.

A New Family of Hybrid Three-Term Conjugate Gradient BNC-BTC Based on Scaled Memoryless BFGS Update for Unconstrained Optimization Problems

Muhammad Aqiil Iqmal Ishak — 2025-06-17

Conjugate gradient (CG) methods are an important enhancement to the category of techniques utilized for resolving unconstrained optimization problems. However, some of the existing CG algorithms are not the most effective solution for all different kinds of problems. Particularly, for some problems, traditional CG techniques may show slower convergence rates or even fail to converge. These inefficiencies frequently result from large-scale issues' incapacity to maintain suitable descent directions or to accurately approximate the Hessian matrix. Hence, this paper introduces a new hybrid CG method for solving unconstrained optimization problems. The method proposed in this study incorporates two parameters, as proposed by Hassan and Alashoor, and aligns with the memoryless Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton approach. This approach satisfies the descent requirement and has the potential to achieve global convergence, presuming that the Wolfe and Armijo-like conditions and any other prerequisite assumptions are satisfied. Numerical experiments on certain benchmark test issues are performed, and the results show that the proposed method is more efficient than other existing methods.

A Mathematical Model of Inventory Cash Flow for Deteriorating Items in a Pharmaceutical Industry

Baba Galadima Agaie — 2025-06-17

A pharmaceutical economic order quantity (EOQ) inventory model is proposed in which the dynamics of the inventory is mainly affected by demand and the rate of deterioration. The deterioration rate is taken to be time dependent, and the time due to deterioration is assumed to follow three-parameter Weibull distribution, the demand rate is price dependent and shortages are allowed and partially backlogged. A simple analytical procedure for deriving the model is provided and also the necessary and sufficient condition for the optimal replenishment policy for the inventory model is established. Finally, a numerical example is provided to illustrate the solution procedure of the model and a comprehensive sensitivity analysis was conducted to analyze the effect of changes in the basic model parameters on the optimal solution.

Meta-Analysis on Substantive Mechanics for Maximizing Productivity and Cost Reciprocity in Routing Optimization

Farid Morsidi — 2025-06-17

In the following article, several conjoining features are discussed for routing optimization by incorporating cost-approximating measures derived from cost-resource allocation reciprocity. To maximize productivity and cost efficiency, the deployment of computational intelligence applications over scheduling systems is coordinated across the optimization process. Several relevant co-works previously envisioning this topic's broad context are discussed as a conjoining point for correlating the expositions of annotating a highly proficient routing system architecture via an emphasis on planning to manage cost allocation within the system domain. There are several collective features presented in this paper for routing optimization that incorporate contributions from cost-approximating measures derived from the reciprocity of cost-resource allocation. Contrasting among the prominent relevant research efforts, which aim to achieve high productivity and cost efficiency, further improvisations of baseline computational intelligence applications initiated among different routing instances were examined with great detail to establish key cost reciprocity features for scheduling models that execute expansive route optimizations over scheduling systems. The expositions on annotating an extensively viable routing system framework were interrelated by examining several pertinent co-works that intended to regulate the allocation of costs as a whole.

Development of E-Waste Management Mobile Application Using Location-based Service

Nik Nahdiya Nik Kamaruzaman — 2025-06-17

The technological revolution has significantly transformed the global landscape. As awareness
of the detrimental impacts of e-waste has grown, people have begun to take this issue seriously.
Nevertheless, several barriers hinder active participation in e-waste recycling, including
challenges in accessing collection points and limited awareness about proper recycling
procedures. Addressing these challenges and improving e-waste recycling practices is of
paramount importance. This study aims to examine a suitable system for managing electronic
waste by developing a mobile application utilizing location-based services to assist users in
recycling e-waste. The project employed the Rapid Application Development (RAD) model, which
emphasizes fast iterations in developing the final product of an e-waste management
application. The results reveal the mobile application user interface for e-waste management
that guides users in managing e-waste properly in terms of finding nearby drop-off location
points. Potential future updates could encompass the integration of points and badge elements,
which has the potential to attract a larger user base. Introducing a feedback and rating
mechanism would also allow users to promptly share their insights and contribute to a
continuous cycle of enhancement and user engagement.

A Three-term PRP-DL Method Modification with Application in Image Denoising Problem

Muhammad Aqiil Iqmal Ishak — 2025-06-17

Image denoising poses a critical challenge due to the impact of noise on image quality and the need to preserve essential details. This study introduces a hybrid Polak-Ribiére-Polyak (PRP)-Dai-Liao (DL) conjugate gradient method with a modified scalar to improve the performance of denoising algorithms on large-scale images. The proposed method involves modifying the scalar in the PRP-DL conjugate gradient method, thereby enhancing algorithmic efficiency, especially in handling large-scale problems. Convergence analysis under the standard Wolfe-Powell line search is established, and numerical results demonstrate that the proposed method is more efficient and robust than existing conjugate gradient methods. The application of the method to image denoising with various noise levels and window sizes confirms its capability to effectively remove noise while preserving image details. Overall, this modified conjugate gradient method shows promise for practical applications in image denoising problem.

The Development of CancerAtlas Data Visualization on Number of Cancer deaths Worldwide Analysis

Isyraf Iskandar — 2025-06-17

Cancer is a highly lethal and challenging disease that has tremendous effects on the death rates of the people across the globe. However, the heterogeneity in cancer types as well as their respective death rates among the regions makes it very difficult to understand the global cancer map. CancerAtlas: The Data Visualization on Cancer Deaths Cases Analysis project aims at raising the awareness about the patterns in cancer death rates worldwide and also regional inequalities. The project uses data analytics to create informative infographics on cancer death in the world, which allows us to reflect this complicated data in an understandable way. Using the CancerAtlas dashboard, users can interactively assess, compare, and analyze the cancer mortality rates in different areas, and different age groups with various types of cancer. Usability testing showed that the dashboard is very user-friendly, and it was so easy to understand the presented information including predictive analysis. This implies that data analytics, data visualization and predictive analysis are very powerful tools for creating public awareness regarding the global cancer trends. Since the CancerAtlas dashboard possesses great potential, it is recommended that future versions of the project include real-time data to deliver live visualizations and forecasts, thereby making the insights obtained from the data more accurate and also relevant.

Keywords: Cancer mortality rates, Data visualization, Predictive analysis, Data analytics

Modeling Road Network in the Main Campus of Universiti Putra Malaysia, Serdang, Selangor, Using Graph Theory

Tan Chai Fang — 2025-06-17

Graph theory is a powerful mathematical tool that can be applied to solve many real-life problems including modeling traffic flows as directed graphs and analyzing them to propose solutions for congestion problems. This research aims to describe the road network of the north and south campuses of Universiti Putra Malaysia, Serdang, Selangor, as a directed graph, consisting of junctions as vertices and interchanges (or links) between junctions with other junctions as directed edges. This research also aims to determine the shortest path between a junction to all other junctions by using two shortest path algorithms, namely the Dijkstra and Floyd-Warshall algorithms, and consequently compare their efficiencies in producing the results. The algorithms are modified so that not only the length of the shortest path is given but also to identify the shortest path itself. Based on the findings of this research, we propose several strategies to minimize traffic congestion, especially during peak hours or convocation sessions, which would benefit both university and surrounding communities.

Modified Quasi-Newton Method via Linear Gradient Flow System

Chui Ying Yap — 2025-06-17

This paper introduces an efficient method for unconstrained optimization based on approximating the gradient flow derived from the objective function. The proposed method uses linear approximation and some quasi-Newton update to approximate the gradient flow, which leads to a modified quasi-Newton BFGS update. An implementation of the proposed method under the line search approach is considered. Numerical results demonstrate that the modified BFGS method is more effective to standard BFGS method.

Optimizing the Number of Hospital’s Emergency Department in Petaling Jaya Using Set Covering Problem Analysis

Muhammad Alif Safwan Bin Mohd Radzi — 2025-06-17

The emergency department (ED) has evolved into a critical frontline service for unscheduled patients who arrive at the hospital and need urgent attention. Its primary function is to diagnose, stabilize and treat the patients facing life-threatening illness, critical injuries, or other urgent medical conditions. EDs operate 24 hours a day for every day a week to provide efficient service to the patients at any time. However, the ED is struggling to provide the service and ensure all potential demand are efficiently covered. This is proved by the previous research that, if the demand is too far from the ED, the mortality rate will increase. Therefore, the main goal of this research is to find the least number of EDs needed as ambulance location and emergency provider for fast response to priority case 1, to cover all potential demand within a certain distance, specifically in Petaling Jaya. The Maximal Covering Location Problem (MCLP) and Maximum Expected Covering Location Problem (MEXCLP) will be used to find the efficiency of the 14 existing EDs required to cover all the demand in Petaling Jaya. The output of this research could be used to identify the optimal number of EDs in Petaling Jaya.