3,386 research outputs found
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
Energy Efficient Scheduling and Routing via Randomized Rounding
We propose a unifying framework based on configuration linear programs and
randomized rounding, for different energy optimization problems in the dynamic
speed-scaling setting. We apply our framework to various scheduling and routing
problems in heterogeneous computing and networking environments. We first
consider the energy minimization problem of scheduling a set of jobs on a set
of parallel speed scalable processors in a fully heterogeneous setting. For
both the preemptive-non-migratory and the preemptive-migratory variants, our
approach allows us to obtain solutions of almost the same quality as for the
homogeneous environment. By exploiting the result for the
preemptive-non-migratory variant, we are able to improve the best known
approximation ratio for the single processor non-preemptive problem.
Furthermore, we show that our approach allows to obtain a constant-factor
approximation algorithm for the power-aware preemptive job shop scheduling
problem. Finally, we consider the min-power routing problem where we are given
a network modeled by an undirected graph and a set of uniform demands that have
to be routed on integral routes from their sources to their destinations so
that the energy consumption is minimized. We improve the best known
approximation ratio for this problem.Comment: 27 page
New complexity results for parallel identical machine scheduling problems with preemption, release dates and regular criteria
In this paper, we are interested in parallel identical machine scheduling
problems with preemption and release dates in case of a regular criterion to be
minimized. We show that solutions having a permutation flow shop structure are
dominant if there exists an optimal solution with completion times scheduled in
the same order as the release dates, or if there is no release date. We also
prove that, for a subclass of these problems, the completion times of all jobs
can be ordered in an optimal solution. Using these two results, we provide new
results on polynomially solvable problems and hence refine the boundary between
P and NP for these problems
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
How the structure of precedence constraints may change the complexity class of scheduling problems
This survey aims at demonstrating that the structure of precedence
constraints plays a tremendous role on the complexity of scheduling problems.
Indeed many problems can be NP-hard when considering general precedence
constraints, while they become polynomially solvable for particular precedence
constraints. We also show that there still are many very exciting challenges in
this research area
Scheduling Bidirectional Traffic on a Path
We study the fundamental problem of scheduling bidirectional traffic along a
path composed of multiple segments. The main feature of the problem is that
jobs traveling in the same direction can be scheduled in quick succession on a
segment, while jobs in opposing directions cannot cross a segment at the same
time. We show that this tradeoff makes the problem significantly harder than
the related flow shop problem, by proving that it is NP-hard even for identical
jobs. We complement this result with a PTAS for a single segment and
non-identical jobs. If we allow some pairs of jobs traveling in different
directions to cross a segment concurrently, the problem becomes APX-hard even
on a single segment and with identical jobs. We give polynomial algorithms for
the setting with restricted compatibilities between jobs on a single and any
constant number of segments, respectively
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