ORCID Profile
0000-0002-1363-2774
Current Organisation
University of New South Wales
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Neural, Evolutionary and Fuzzy Computation | Artificial Intelligence and Image Processing
Productivity (excl. Public Sector) | Expanding Knowledge in the Information and Computing Sciences |
Publisher: IEEE
Date: 04-2013
Publisher: Elsevier BV
Date: 02-2021
Publisher: Informa UK Limited
Date: 05-2000
Publisher: IEEE
Date: 12-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 10-2016
Publisher: IEEE
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 03-2006
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 12-1999
Publisher: Elsevier BV
Date: 04-2002
Publisher: World Scientific Pub Co Pte Ltd
Date: 10-2011
DOI: 10.1142/S0218213011000322
Abstract: Biological networks are structurally adaptive and take on non-random topological properties that influence system robustness. Studies are only beginning to reveal how these structural features emerge, however the influence of component fitness and community cohesion (modularity) have attracted interest from the scientific community. In this study, we apply these concepts to an evolutionary algorithm and allow its population to self-organize using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based topological operators for guiding network structural dynamics, which in turn are guided by population changes taking place over evolutionary time. To investigate the effect on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and panmictic evolutionary algorithm designs. Our results suggest that a self-organizing topology evolutionary algorithm can exhibit robust search behavior with strong performance observed over short and long time scales. More generally, the coevolution between a population and its topology may constitute a promising new paradigm for designing adaptive search heuristics.
Publisher: IEEE
Date: 05-2015
Publisher: Elsevier BV
Date: 07-2002
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2006
DOI: 10.1109/TSMCB.2005.855569
Abstract: Red teaming is the process of studying a problem by anticipating adversary behaviors. When done in simulations, the behavior space is ided into two groups one controlled by the red team which represents the set of adversary behaviors or bad guys, while the other is controlled by the blue team which represents the set of defenders or good guys. Through red teaming, analysts can learn about the future by forward prediction of scenarios. More recently, defense has been looking at evolutionary computation methods in red teaming. The fitness function in these systems is highly stochastic, where a single configuration can result in multiple different outcomes. Operational, tactical and strategic decisions can be made based on the findings of the evolutionary method in use. Therefore, there is an urgent need for understanding the nature of these problems and the role of the stochastic fitness to gain insight into the possible performance of different methods. This paper presents a first attempt at characterizing the search space difficulties in red teaming to shed light on the expected performance of the evolutionary method in stochastic environments.
Publisher: Penerbit UTM Press
Date: 21-06-2016
DOI: 10.11113/JT.V78.9162
Abstract: Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.
Publisher: Elsevier BV
Date: 02-2017
Publisher: Springer Science and Business Media LLC
Date: 02-02-2018
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Trans Tech Publications, Ltd.
Date: 02-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.903.402
Abstract: Irrespective of the type of items manufactured by an industry, environment is now becoming progressively more and more competitive than the past few decades. To sustain in this severe competition, companies have no choice but to manage their operations optimally and in this respect the importance of more accurate demand prediction cannot be exaggerated. This research presents a forecasting approach tailoring the multiplicative Holt-Winters method with growth adjustment through incorporation of fuzzy logic. The growth parameter of the time series values is adjusted with the conventional Holt-Winters method and tested for predicting the real-life demand of transformer tank experienced by a local company. The result obtained by applying the new approach shows a significant improvement in the accuracy of the forecasted demand and sheds light on further enhancement of the proposed method by optimizing other time series parameters through fuzzy logic application for possible application in prediction of demand having trend, seasonal and cyclical changes.
Publisher: Elsevier BV
Date: 10-2015
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Informa UK Limited
Date: 15-08-2011
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 03-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2013
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 12-2019
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 12-2017
Publisher: IEEE
Date: 06-2012
Publisher: Springer Science and Business Media LLC
Date: 06-2014
Publisher: IEEE
Date: 2003
Publisher: ACM
Date: 06-07-2013
Publisher: Elsevier BV
Date: 02-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Elsevier BV
Date: 10-2019
Publisher: Elsevier BV
Date: 12-2017
Publisher: Springer International Publishing
Date: 27-12-2017
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier BV
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-640-2.CH003
Abstract: Red teaming is the process of studying a problem by anticipating adversary behaviors. When done in simulations, the behavior space is ided into two groups: one controlled by the red team, which represents the set of adversary behaviors or bad guys the other controlled by the blue team, which represents the set of defenders or good guys. Through red teaming, analysts can learn about the future by forward prediction of scenarios. More recently, defense has been looking at evolutionary computation methods in red teaming. The fitness function in these systems is highly stochastic, where a single configuration can result in multiple outcomes. Operational, tactical and strategic decisions can be made based on the findings of the evolutionary method in use. Therefore, there is an urgent need to understand the nature of these problems and the role of the stochastic fitness to gain insight into the possible performance of different methods. This chapter presents a first attempt at characterizing the search space difficulties in red teaming to shed light on the expected performance of the evolutionary method in stochastic environments.
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 05-2009
Publisher: Wiley
Date: 26-01-2012
DOI: 10.1002/WCM.1247
Publisher: Elsevier BV
Date: 09-2015
Publisher: IEEE
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: IEEE
Date: 06-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 03-2021
Publisher: Springer Science and Business Media LLC
Date: 07-05-2019
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 12-2015
Publisher: Elsevier BV
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Inderscience Publishers
Date: 2010
Publisher: IEEE
Date: 09-2007
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 07-2014
Publisher: Cambridge University Press (CUP)
Date: 06-06-2017
DOI: 10.1017/S0890060416000196
Abstract: The aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper.
Publisher: Elsevier BV
Date: 03-2013
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Informa UK Limited
Date: 05-11-2014
Publisher: IEEE
Date: 06-2013
Publisher: Elsevier BV
Date: 11-0011
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 12-2014
Publisher: Elsevier BV
Date: 09-2014
Publisher: Elsevier BV
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Elsevier BV
Date: 10-2012
Publisher: Elsevier BV
Date: 09-2011
Publisher: Inderscience Publishers
Date: 2009
Publisher: IEEE
Date: 07-2016
Publisher: IGI Global
Date: 2006
Publisher: IEEE
Date: 07-2014
Publisher: Elsevier BV
Date: 05-2016
Publisher: Elsevier BV
Date: 09-2012
Publisher: Springer Science and Business Media LLC
Date: 24-04-2011
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: IEEE
Date: 2003
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-848-2.CH001
Abstract: Artificial neural network (ANN) is one of the main constituents of the artificial intelligence techniques. Like in many other areas, ANN has made a significant mark in the domain of healthcare applications. In this chapter, we provide an overview of the basics of neural networks, their operation, major architectures that are widely employed for modeling the input-to-output relations, and the commonly used learning algorithms for training the neural network models. Subsequently, we briefly outline some of the major application areas of neural networks for the improvement and well being of human health.
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 12-2011
Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 04-2002
Publisher: IEEE
Date: 2007
DOI: 10.1109/ICIS.2007.9
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 05-02-2018
Publisher: ACTAPRESS
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 26-06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Elsevier BV
Date: 11-2012
Publisher: IEEE
Date: 2007
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-848-2.CH010
Abstract: This chapter provides an overview of artificial neural network applications for the detection and classification of various gaits based on their typical characteristics. Gait analysis is routinely used for detecting abnormality in the lower limbs and also for evaluating the progress of various treatments. Neural networks have been shown to perform better compared to statistical techniques in some gait classification tasks. Various studies undertaken in this area are discussed with a particular focus on neural network’s potential in gait diagnostics. Ex les are presented to demonstrate the suitability of neural networks for automated recognition of gait changes due to aging from their respective gait patterns and their potential for identification of at-risk or non-functional gait.
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 05-2010
Publisher: Informa UK Limited
Date: 2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2017
DOI: 10.3934/JIMO.2018091
Publisher: Informa UK Limited
Date: 09-07-2015
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 10-2008
Publisher: Springer Science and Business Media LLC
Date: 17-05-2011
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 02-2013
Publisher: Springer Science and Business Media LLC
Date: 07-01-2016
Publisher: IEEE
Date: 05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer International Publishing
Date: 2016
Publisher: MDPI AG
Date: 06-04-2018
DOI: 10.3390/A11040043
Publisher: Springer Science and Business Media LLC
Date: 04-2009
Publisher: Elsevier BV
Date: 08-2016
Publisher: IEEE
Date: 2003
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-670-9.CH008
Abstract: In today’s global market economy, currency exchange rates play a vital role in national economy of the trading nations. In this chapter, we present an overview of neural network-based forecasting models for foreign currency exchange (forex) rates. To demonstrate the suitability of neural network in forex forecasting, a case study on the forex rates of six different currencies against the Australian dollar is presented. We used three different learning algorithms in this case study, and a comparison based on several performance metrics and trading profitability is provided. Future research direction for enhancement of neural network models is also discussed.
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 06-2013
Publisher: Springer Science and Business Media LLC
Date: 25-08-2011
Abstract: Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships. In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we ide the sequences vertically into two or more subsequences, and then solve them in idually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. The experimental results showed that the VDGA with three vertical isions was the most successful variant for most of the test cases in comparison to other isions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research.
Publisher: Springer Science and Business Media LLC
Date: 12-12-2009
Publisher: Elsevier BV
Date: 12-1994
Publisher: Elsevier BV
Date: 04-2014
Publisher: Elsevier BV
Date: 02-2017
Publisher: Elsevier BV
Date: 02-2015
Publisher: Elsevier BV
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Elsevier BV
Date: 2015
Publisher: IEEE
Date: 07-2014
Publisher: Springer Science and Business Media LLC
Date: 09-08-2008
Publisher: Springer Science and Business Media LLC
Date: 12-07-0015
Publisher: IEEE
Date: 11-2012
DOI: 10.1109/GCIS.2012.95
Publisher: IEEE
Date: 06-2011
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-670-9.CH001
Abstract: The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-670-9.CH002
Abstract: Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modeling complex real-world problems. One of the latest research areas in this field is evolving ANNs. In this chapter, we investigate the simultaneous evolution of network architectures and connection weights in ANNs. In simultaneous evolution, we use the well-known concept of multiobjective optimization and subsequently evolutionary multiobjective algorithms to evolve ANNs. The results are promising when compared with the traditional ANN algorithms. It is expected that this methodology would provide better solutions to many applications of ANNs.
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2014
Publisher: Elsevier BV
Date: 10-2017
Publisher: Elsevier BV
Date: 02-2013
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 09-2017
Publisher: Elsevier BV
Date: 08-2014
Publisher: IEEE
Date: 10-2019
Publisher: Elsevier BV
Date: 10-2009
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: American Society of Clinical Oncology (ASCO)
Date: 11-2018
DOI: 10.1200/PO.18.00085
Publisher: Elsevier BV
Date: 05-2002
Publisher: Elsevier BV
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 09-2007
Publisher: IEEE
Date: 07-2018
Publisher: MIT Press - Journals
Date: 03-2013
DOI: 10.1162/EVCO_A_00064
Abstract: In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil recovery (EOR). The total gas that can be used for oil extraction is constrained by daily availability limits. The oil extracted from each well is known to be a nonlinear function of the gas injected into the well and varies between wells. The problem is to identify the optimal amount of gas that needs to be injected into each well to maximize the amount of oil extracted subject to the constraint on the total daily gas availability. The problem has long been of practical interest to all major oil exploration companies as it has the potential to derive large financial benefit. In this paper, an infeasibility driven evolutionary algorithm is used to solve a 56 well reservoir problem which demonstrates its efficiency in solving constrained optimization problems. Furthermore, a multi-objective formulation of the problem is posed and solved using a number of algorithms, which eliminates the need for solving the (single objective) problem on a regular basis. Lastly, a modified single objective formulation of the problem is also proposed, which aims to maximize the profit instead of the quantity of oil. It is shown that even with a lesser amount of oil extracted, more economic benefits can be achieved through the modified formulation.
Publisher: IEEE
Date: 04-2007
Publisher: Elsevier BV
Date: 04-2002
Publisher: Elsevier BV
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: IEEE
Date: 11-2013
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer New York
Date: 2009
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
Publisher: Informa UK Limited
Date: 02-1991
DOI: 10.1057/JORS.1991.27
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 06-10-2010
Publisher: Elsevier BV
Date: 2014
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2004
DOI: 10.1142/S0217595904000217
Abstract: The use of evolutionary strategies (ESs) to solve problems with multiple objectives [known as vector optimization problems (VOPs)] has attracted much attention recently. Being population-based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto-frontier differential evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for two standard test problems, outperform the "strength Pareto evolutionary algorithm", one of the state-of-the-art evolutionary algorithm for solving VOPs.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: World Scientific Pub Co Pte Lt
Date: 10-2011
DOI: 10.1142/S0217595911003442
Abstract: Surface mining, often adopted for exploiting natural resources all over the world, is a major subject of debate as it causes major environmental impacts. It not only adversely alters the landscape but it also seriously h ers the traditional living conditions of numerous inhabitants, who may be displaced against their wishes without receiving necessary compensation. In this paper, goal programming is combined with the analytic hierarchy process to determine optimal decisions for the planned relocation of people where surface mining may take place in a densely populated environment, while addressing multiple conflicting objectives. The combined approach is illustrated with a numerical ex le highlighting its usage in other decision problems.
Publisher: Springer Science and Business Media LLC
Date: 21-05-2018
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2015
DOI: 10.1142/S146902681550025X
Abstract: Many infrastructures, such as those of finance and banking, transportation, military and telecommunications, are highly dependent on the Internet. However, as the Internet’s underlying structural protocols and governance can be disturbed by intruders, for its smooth operation, it is important to minimize such disturbances. Of the available techniques for achieving this, computational intelligence methodologies, such as evolutionary algorithms and swarm intelligence approaches, are popular and have been successfully applied to detect intrusions. In this paper, we present an overview of these techniques and related literature on intrusion detection, analyze their research contributions, compare their approaches and discuss new research directions which will provide useful insights for intrusion detection researchers and practitioners.
Publisher: Elsevier BV
Date: 10-2003
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 09-2016
Publisher: IEEE
Date: 10-2008
Publisher: Elsevier BV
Date: 04-2000
Publisher: Elsevier BV
Date: 10-2017
Publisher: IEEE
Date: 09-2007
Publisher: IEEE
Date: 07-2010
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 11-2017
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: SPIE
Date: 28-12-2005
DOI: 10.1117/12.644273
Publisher: Elsevier BV
Date: 12-2018
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Elsevier BV
Date: 05-2015
Publisher: Elsevier BV
Date: 03-2019
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 09-2008
Publisher: Cambridge University Press (CUP)
Date: 09-06-2016
DOI: 10.1017/S0890060415000323
Abstract: The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.
Publisher: Elsevier BV
Date: 05-2019
Publisher: Elsevier BV
Date: 03-2014
Publisher: IEEE
Date: 06-2017
Publisher: Elsevier BV
Date: 08-2014
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 11-2015
Publisher: AIP
Date: 2010
DOI: 10.1063/1.3314271
Publisher: Springer Science and Business Media LLC
Date: 04-10-2013
Publisher: Springer Berlin Heidelberg
Date: 2012
Start Date: 04-2019
End Date: 12-2023
Amount: $568,410.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2021
End Date: 12-2024
Amount: $754,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2017
End Date: 03-2020
Amount: $360,500.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2015
End Date: 12-2017
Amount: $236,700.00
Funder: Australian Research Council
View Funded Activity