Menu Planning Model for Malaysian Boarding School Using Self-Adaptive Hybrid Genetic Algorithms

Malnutrition problem is the gravest single threat to the world's public health today. Statistics have showed that the number of under-nourished and over-nourished children and adolescents is increasing day by day. Thus, proper menu planning process among menu planners or caterers is important to avoid some diet-related diseases in the hture. Manual calculation of menu planning is unable to consider macronutrients and micronutrients simultaneously due to complexities of data and length of time. In this study, self-adaptive hybrid genetic algorithm (SHGA) approach has been proposed to solve the menu planning problem for Malaysian boarding school students aged 13 to 18 years old. The objectives of our menu planning model are to optimize the budget allocation for each student, to take into consideration the caterer's ability, to llfill the standard recommended nutrient intake (RNI) and maximize the variety of daily meals. New local search was adopted in this study, the insertion search with delete-and-create (ISDC) method, which combined the insertion search (IS) and delete-and-create (DC) local search method. The implementation of IS itself could not guarantee the production of feasible solutions as it only explores a small neighborhood area. Thus, the ISDC was utilized to enhance the search towards a large neighborhood area and the results indicated that the proposed algorithm is able to produce 100% feasible solutions with the best fitness value. Besides that, implementation of self-adaptive probability for mutation has significantly minimized computational time taken to generate the good solutions in just few minutes. Hybridization technique of local search method and self-adaptive strategy have improved the performance of traditional genetic algorithm through balanced exploitation and exploration scheme. Finally, the present study has developed a menu planning prototype for caterers to provide healthy and nutritious daily meals using simple and fhendly user interface

A heuristics approach for classroom scheduling using genetic algorithm technique

Reshuffling and arranging classroom based on the capacity of the audience, complete facilities, lecturing time and many more may lead to a complexity of classroom scheduling. While trying to enhance the efficiency in classroom planning, this paper proposes a heuristic approach for timetabling optimization. A new algorithm was produced to take care of the timetabling problem in a university. The proposed of heuristics approach will prompt a superior utilization of the accessible classroom space for a given time table of courses at the university. Genetic Algorithm through Java programming languages were used in this study and aims at reducing the conflicts and optimizes the fitness. The algorithm considered the quantity of students in each class, class time, class size, time accessibility in each class and lecturer who in charge of the classes

PENCAPAIAN KURSUS MATEMATIK DAN STATISTIK DI KALANGAN PELAJAR UTHM: FAKTOR MEMPENGARUHI DAN TEKNIK PENGAJARAN DAN PEMBELAJARAN YANG LEBIH DIMINATI

Kajian ini bertujuan mengenal pasti punca yang mempengaruhi pencapaian kursus matematik dan statistik di kalangan pelajar Universiti Tun Hussein Onn Malaysia (UTHM). Seramai 2605 orang pelajar telah terlibat dalam kajian ini dan pemilihan responden adalah dilakukan secara rawak berstrata berdasarkan jumlah populasi pelajar di setiap fakulti. Kajian ini menggunakan statistik deskriptif analisis seperti min, sisihan piawai, kekerapan dan peratus untuk menjelaskan faktor-faktor yang mempengaruhi pencapaian pelajar dalam kursus matematik dan statistik. Seterusnya, kajian ini juga mengenalpasti kaedah dan teknik pembelajaran yang lebih diminati pelajar dan dianalisis dengan menggunakan kaedah analisis Pareto. Hasil kajian mendapati faktor latar belakang diri, faktor tenaga pengajar, dan faktor kondisi tempat pengajian mempengaruhi pencapaian pelajar dalam kursus matematik dan statistik manakala kaedah pembelajaran berpusatkan pelajar adalah lebih diminati oleh pelajar berbanding pembelajaran berpusatkan guru

An efficient algorithm to improve oil-gas pipelines path

Oil-gas pipeline is a complex and high-cost system in terms of materials, construction, maintenance, control, and monitoring in which it involves environmental, economic and social risk. In the case study of Iraq, this system of pipelines is above the ground and is liable to accidents that may cause environmental disaster, loss of life and money. Therefore, the aim of this study is to propose a new algorithm to obtain the shortest path connecting oil-gas wells and addressing obstacles that may appear on the path connecting any two wells. In order to show the efficiency of the proposed algorithm, comparison between ant colony optimization (ACO) algorithm and a real current meth-od of linking is used for this purpose. Result shows that the new proposed algorithm outperformed the other methods with higher reduc-tion in operational cost by 16.4% for a number of 50 wells. In addition, the shortest path of connecting oil-gas wells are able to overcome all the addressed obstacles in the Rumaila north field, which is located in the city of Basra in southern Iraq

A scheduling problem for hospital operating theatre

This paper provides a classification of real scheduling problems. Various ways have been examined and described on the problem. Scheduling problem faces a tremendous challenges and difficulties in order to meet the preferences of the consumer. Dealing with scheduling problem is complicated, inefficient and time-consuming. This study aims to develop a mathematical model for scheduling the operating theatre during peak and off peak time. Scheduling problem is a well known optimization problem and the goal is to find the best possible optimal solution. In this paper, we used integer linear programming technique for scheduling problem in a high level of synthesis. In addition, time and resource constrained scheduling was used. An optimal result was fully obtained by using the software GLPK/AMPL. This model can be adopted to solve other scheduling problems, such as the Lecture Theatre, Cinemas and Work Shift

Emotional intelligence of Malaysian university students using non-parametric approach

The purposes of present study are to investigate the level of emotional intelligence among university students, identify the significant differences of emotional intelligence with demographic background and measure the relationship of emotional intelligence with academic performance. Non-parametric methods which are Mann-Whitney test, Kruskal-Wallis test and Spearman Correlation test were used to analyze 400 data from eight different faculties. Results found that the level of emotional intelligence among university students is at average or above level. The different of age, ethnic, faculty, hometown location and family income have significant difference in emotional intelligence. Besides that, there are strong positive relationship between emotional intelligence and academic performance among university students

Optimized preference of security staff scheduling using integer linear programming approach

This paper proposes an optimized schedule for security staff using integer linear programming approach. It is important to improve the life quality of the security staff since the negative social life such as family problems, less social support or even stress following from a poor work schedule. Therefore, this study aims to maximize the preference satisfaction of the security staff by allowing them to choose their preferred shift and day off while taking into consideration the restrictions of the university rules. The mathematical model of integer linear programming approach is developed and solved by using LPSolve IDE package. The result shows the overall preference satisfaction of the security staff towards work shift and days off is successfully maximized from 228.33 to 394.33. The comparison of the real schedule and the new proposed optimized schedule is made and all the constraints are successfully satisfied. The proposed schedule able to assist the university management in producing the most flexible and beneficial schedule for their staff to increase the satisfaction towards the working life

The effective model of linear regressions for colorectal cancer stages in general hospital: a case study in Kuala Lumpur

Regression analysis has become popularity among several sources of researcher and standard tolls in analyzing data. This structure was represented two commonly statistical models such as multiple linear regression and extended fuzzy correlation and regression analysis (Ni, 2005). Colorectal cancer was applied and case in Malaysia. The quality of life in CRC patients to detect the early CRC stage is still very poor, mainly ad-hoc and not implemented as a national wide programme. This study aims to determine the best model to measuring the mortality rate of patients by CRC stages at hospital using mean square error compared. Secondary data of 180 patients have colorectal cancer and received treatment in hospital recorded by nurses and doctors. Based on the results of regression, extended fuzzy correlation and regression analysis (Ni, 2005) is the best model to measuring the mortality rate of patients who have colorectal cancer after received treatment in hospital

Integer linear programming on preference maximized of workforce scheduling

This study investigated the application of integer linear programming with the purpose of solving workforce scheduling problems in real life and satisfying the constraints at the same time, which includes the staff preferences towards shift and company policies. Integer linear programming is a well-known mathematical approach which is able to obtain the optimal solution faster than manually schedule construction in a less-complex way. In this study, a weekly schedule which involved thirteen staff is obtained successfully by using integer linear programming approach through the help of LP Solve IDE software. The result showed that the total staffing cost was successfully minimized due to reduced number of full shift assigned to staff. In addition, the staff satisfaction is maximized by satisfying all their preferences

The Use of Fuzzy Linear Regression Modeling to Predict High-risk Symptoms of Lung Cancer in Malaysia

Lung cancer is the most prevalent cancer in the world, accounting for 12.2% of all newly diagnosed cases in 2020 and has the highest mortality rate due to its late diagnosis and poor symptom detection. Currently, there are 4,319 lung cancer deaths in Malaysia, representing 2.57 percent of all mortality in 2020. The late diagnosis of lung cancer is common, which makes survival more difficult. In Malaysia, however, most cases are detected when the tumors have become too large, or cancer has spread to other body areas that cannot be removed surgically. This is a frequent situation due to the lack of public awareness among Malaysians regarding cancer-related symptoms. Malaysians must be acknowledged the highrisk symptoms of lung cancer to enhance the survival rate and reduce the mortality rate. This study aims to use a fuzzy linear regression model with heights of triangular fuzzy by Tanaka (1982), H-value ranging from 0.0 to 1.0, to predict high-risk symptoms of lung cancer in Malaysia. The secondary data is analyzed using the fuzzy linear regression model by collecting data from patients with lung cancer at Al-Sultan Abdullah Hospital (UiTM Hospital), Selangor. The results found that haemoptysis and chest pain has been proven to be the highest risk, among other symptoms obtained from the data analysis. It has been discovered that the H-value of 0.0 has the least measurement error, with mean square error (MSE) and root mean square error (RMSE) values of 1.455 and 1.206, respectively