Mohd Salihin Ngadiman

Review of state of the art for metaheuristic techniques in Academic Scheduling Problems

The Academic Scheduling Problems have drawn great interest from many researchers of various fields, such as operational research and artificial intelligence. Despite the long history of literature, the problem still remains as an interesting research topic as new and emerging metaheuristic techniques continue to exhibit promising results. This paper surveys the properties of the Academic Scheduling Problems, such as the complexity of the problem and the constraints involved and addresses the various metaheuristic techniques and strategies used in solving them. The survey in this paper presents the aspects of solution quality in terms of computational speed, feasibility and optimality of a solution.

An Alternative Model for ERP Maintenance Strategy

For the past few years, it has been possible to buy a business application including transaction processing systems for such tasks as accounting, manufacturing, or human resources as a packaged product. Packages to do this collection of work are generally referred to as enterprise resource planning (ERP) systems. Most ERP systems are huge because of the diversity of tasks they must perform. The ERP systems are providing an integration of several tasks, and the flexibility to perform those tasks at enterprises with vastly varying needs. But, only few of these ERP systems developed have actually considered maintenance strategies. Maintenance is a complex process that is triggered by planned periodic repair (scheduled or planned maintenance), equipment breakdown or deterioration indicated by a monitored parameter (unplanned or emergency maintenance). This process includes planning, scheduling, monitoring, quality assurance and the development of necessary resources such as workshop, labor, machines, equipment, tools, spare parts and materials.

AN ADAPTED CUCKOO OPTIMIZATION ALGORITHM AND GENETIC ALGORITHM APPROACH TO THE UNIVERSITY COURSE TIMETABLING PROBLEM

The university course timetabling problem is an NP-hard and NP-complete problem concerned with assigning a specific set of events and resources to timeslots under a highly-constrained search space. This paper presents a novel metaheuristic algorithm entitled adapted cuckoo optimization algorithm which is derived from the cuckoo optimization algorithm and cuckoo search algorithm. This algorithm includes features such as local random walk on discrete data which mimics the behavior of Levy flights and an Elitism-based mechanism which echoes back the best candidate solutions and prevents the algorithm from plunging into a curse of dimensionality. The algorithm was tested on a problem instance gathered from a University in Malaysia and the results indicate that the proposed algorithm exhibits very promising results in terms of solution quality and computational speed when compared to genetic algorithms.

Modelling Uncertainty Factors in Environmental Issues on Late Delivery for Construction Industry: A Propose

A very limited research of fixing uncertainties in environmental issues (EI) actually encountered biggest problems for company, especially in late delivery (LD) of project completion for construction industry (CI). Uncertainty could be happens causes by many factors which known or unknown, statically to totally ignorance. Many factors of uncertainties have been modelled in previous research by ignored factors in EI. However, in the real cases all the factors must be manage even it is in-deterministic. Therefore, the modelling of uncertainty factors in EI for CI is very important to be studied, and it will be considered to be used as guidance for decision makers while they are facing with the problems that related to uncertainties. The uncertainty of EI can split into 2 categories; acts of God and acts of humans.. Preliminary result of conceptual model for uncertainty factors in EI is presented according to previous case study.

Managing Uncertainty in Multi-project Constructing for Environmental Issues on Project Completion Late Delivery

Modern construction industry (CI) has many underlying causes of uncertainty that impact on late delivery of project completion’s performance. A comprehensive literature review found that many researchers from many countries were using various techniques to tackle the uncertainty with less consciousness on environmental issues. Uncertainty modelling on project completion late delivery (PCLD) in environmental issues is intentionally to manage the project performance, hence to maximize the customer’s satisfaction. From the research surveys, the relationships between cause and effect that underlying uncertainty in PCLD in environmental issues are constructed via conceptual mode, in this paper. It involves piping’s CI for water supply company with multi-project construction environment. The uncertainty factors are modelled by diagnosing the significant factors with fractional factorial design using analysis of variance. Next, simulation modelling and experimental study of the underlying causes of uncertainty in PCLD based on real-case study verify and validate this suggestion. Consequently, the model of uncertainty will provide the industries with a reference on the underlying causes of uncertainty that must be tackled with higher priority and may avoid the estimation on lead time.

Integrating a Repairing-based Genetic Algorithm-Neighborhood Search Structure in Solving the Course Timetabling Problem

The course timetabling problem is not a trivial task as it is an NP-hard and NP-complete problem and many solutions have been proposed due to its high complexity search landscape. In essence, the nature of the course timetabling problem is to assign a lecturer-course entity to existing teaching venue and timeslot in an academic institution. In this article, the authors propose a Genetic Algorithm-Neighborhood Search (GANS) to construct a feasible timetable for courses offered by a department in the faculty of a local university in Malaysia. The framework of the solution is as follow: The feasible timetable is first constructed by Genetic Algorithm, which includes are pair operator which attempts to repair infeasible timetables. Upon feasibility, the second phase exploits the initial feasible solution using three neighborhood structures to search for an improved solution and global optimum. The experimental results demonstrate the efficiency and effectiveness of the various neighborhood structures in exploiting the feasible solutions to yield the global optimum.

An arbitrary heuristic room matching algorithm in obtaining an enhanced initial seed for the university course timetabling problem

The curriculum-based course timetabling problem is a subset of the university course timetabling problem which is often regarded as both an NP-hard and NP-complete problem. The nature of the problem concerns with the assignment of lecturers-courses to available teaching space in an academic institution. The curriculum-based course timetabling problem confronts the problem of a multi-dimensional search space and matrices of high conflict-density, thus impeding the task to search for an improved solution. In this paper, the authors propose an arbitrary heuristic room matching algorithm which attempts to improve the initial seed of the curriculum-based course timetabling problem. The objective is to provide a reasonably advantageous search point to perform any subsequent improvement phase and the results obtained indicate that the proposed matching algorithm is able to provide very promising results as the fitness score of the solution is significantly enhanced within a short period of time.