9,616 research outputs found
A New Job Shop Heuristic Algorithm for Machine Scheduling Problems
The purpose of this research is to present a straightforward and relatively efficient method for solving scheduling problems. A new heuristic algorithm, with the objective of minimizing the makespan, is developed and presented in this paper for job shop scheduling problems (JSP). This method determines jobsâ orders for each machine. The assessment is based on the combination of dispatching rules e.g. the Shortest Processing Time of each operation, the Earliest Due Date of each job, the Least Tardiness of the operations in each sequence and the First come First Serve idea. Also, unlike most of the heuristic algorithms, due date for each job, prescribed by the user, is considered in finding the optimum schedule. A multitude of JSP problems with different features are scheduled based on this proposed algorithm. The models are also solved with Shifting Bottleneck algorithm, known as one of the most common and reliable heuristic methods. The result of comparison between the outcomes shows that when the number of jobs are less than or equal to the number of machines, the proposed algorithm concludes smaller, and better, makespan in a significantly lower computational time, which shows the superiority of the suggested algorithm. In addition, for a category when the number of jobs are greater than the number of machines, the suggested algorithm generates more efficient results when the ratio of the number of jobs to the number of machines is less than 2.1. However, in this category for the mentioned ratio to be higher than 2.1, the smaller makespan could be generated by either of the methods, and the results do not follow any particular trend, hence, no general conclusions can be made for this case
Spatial-temporal data modelling and processing for personalised decision support
The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less
Keywords
Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio
Spatial-temporal data modelling and processing for personalised decision support
The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less
Keywords
Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio
Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling
Copyright @ 2000 IEEEThis paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several
heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve
the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed
neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.This work was supported by the Chinese National Natural Science Foundation under Grant 69684005 and the Chinese National High-Tech Program under Grant 863-511-9609-003, the EPSRC under Grant GR/L81468
Efficient heuristics for the parallel blocking flow shop scheduling problem
We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them âespecially the IGAâ perform considerably better than those algorithms adapted from the DPFSP literature.Peer ReviewedPostprint (author's final draft
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
Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks
This paper considers multiprocessor task scheduling in a multistage hybrid
flow-shop environment. The problem even in its simplest form is NP-hard in the
strong sense. The great deal of interest for this problem, besides its
theoretical complexity, is animated by needs of various manufacturing and
computing systems. We propose a new approach based on limited discrepancy
search to solve the problem. Our method is tested with reference to a proposed
lower bound as well as the best-known solutions in literature. Computational
results show that the developed approach is efficient in particular for
large-size problems
An improved constraint satisfaction adaptive neural network for job-shop scheduling
Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and in part by the National Nature Science Fundation of China under Grant 60821063 and National Basic Research Program of China under Grant 2009CB320601
Survival and disinfection of SARS-Cov-2 in environment and contaminated surface
The detection of SARS-Cov-2 in the sewage and water resources has increased the awareness among the people about the possibility survival of SARS-Cov-2 in the environment and the potential to transmit into the human through food chain or water resources. Moreover, the surface contaminated by the virus need to be disinfected frequently by using an effective disinfectant, the current chapter discussed the efficiency of the most traditional treatment process of the sewage and wastewater, and their role in the elimination of the virus as well as the sterility assurance level concept. Moreover, the chemical disinfectant used currently and their temporary efficiency has been reviewed
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