Framework for sustainable TVET-Teacher Education Program in Malaysia Public Universities

Studies had stated that less attention was given to the education aspect, such as teaching and learning in planning for improving the TVET system. Due to the 21st Century context, the current paradigm of teaching for the TVET educators also has been reported to be fatal and need to be shifted. All these disadvantages reported hindering the country from achieving the 5th strategy in the Strategic Plan for Vocational Education Transformation to transform TVET system as a whole. Therefore, this study aims to develop a framework for sustainable TVET Teacher Education program in Malaysia. This study had adopted an Exploratory Sequential Mix-Method design, which involves a semi-structured interview (phase one) and survey method (phase two). Nine experts had involved in phase one chosen by using Purposive Sampling Technique. As in phase two, 118 TVET-TE program lecturers were selected as the survey sample chosen through random sampling method. After data analysis in phase one (thematic analysis) and phase two (Principal Component Analysis), eight domains and 22 elements have been identified for the framework for sustainable TVET-TE program in Malaysia. This framework was identified to embed the elements of 21st Century Education, thus filling the gap in this research. The research findings also indicate that the developed framework was unidimensional and valid for the development and research regarding TVET-TE program in Malaysia. Lastly, it is in the hope that this research can be a guide for the nations in producing a quality TVET teacher in the future

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

A study of project planning on Libyan construction projects

Construction projects are regularly faced by scheduling problems causing the projects to finish beyond their predetermined due date; this is a global phenomenon. The main purpose of this study is to consider the problems associated with project planning generally, with specific reference to construction projects in Libya. This study is unique in two respects. First, despite the recent high volume of infrastructure work in the country, there have been few investigations into construction delays in Libya. Secondly, earlier studies have considered the causes or the effects of project delays, whereas the present aim is to evaluate the potential of applying a planning and scheduling technique that is entirely novel in the Libyan context. The paper reports the results of Phase I of this research

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

A TSSA algorithm based approach to enhance the performance of warehouse system

In this plethora of increased competitiveness and globalization the effective management of the warehouse system is a challenging task. Realizing that proper scheduling of the warehouses is necessary to outperform the competitors on cost, lead time, and customer service basis (Koster, 1998); the proposed research focuses on optimization of warehouse scheduling problems. This research aims to minimize the total tardiness so that the overall time involved in managing the inventory inside the warehouse could be effectively reduced. This research also deals with the vehicle routing issues in the warehousing scenario and considers various constraints, and decision variables, directly influencing the undertaken objective so as to make the model more realistic to the real world environment. The authors have also proposed a hybrid tabu sample-sort simulated annealing (TSSA) algorithm to reduce the tardiness as well as to enhance the performance of the warehousing system. The proposed TSSA algorithm inherits the merits of the tabu search and sample-sort annealing algorithm. The comparative analysis of the results of the TSSA algorithm with other algorithms such as simulated annealing (SA), tabu search (TS), and hybrid tabu search algorithms indicates its superiority over others, both in terms of computational time as well as total tardiness reduction