M. Ayob - Academia.edu (original) (raw)
Papers by M. Ayob
According to Tucker (1982), in a span of 25 years after the second world war, various technologic... more According to Tucker (1982), in a span of 25 years after the second world war, various technological media have been used in teaching and learning at institutions of higher learning such as television, video tapes, slides, audio tapes, including computers. At the end of 1980s until ...
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT Multi parent crossover has been successfully applied to solve many combinatorial optimiz... more ABSTRACT Multi parent crossover has been successfully applied to solve many combinatorial optimization problems such as unconstrained binary quadratic programming problem (UBQP). This because using more than two parents has increased the intensification process by exploiting the information shared by multi parents. However not all type of crossovers are suitable to solve vehicle routing problem (VRP). Therefore, this work introduces a multi parent insertion crossover in solving vehicle routing problem with time windows (VRPTW) by enhancing two parent insertion crossovers. This crossover exchange information among three parents instead of two. Result tested on Solomon VRPTW benchmarks demonstrate that multi parent crossover outperformed two parent crossover on same instances. This prove the effectiveness of having more parents for crossover that can be help the search to find better quality solution.
This work presents a simple graph based heuristic that employs a roulette wheel selection mechani... more This work presents a simple graph based heuristic that employs a roulette wheel selection mechanism for solving examination timetabling problems. We arrange exams in non-increasing order of the number of conflicts (degree) that they have with other exams. The difficulty of each exam to be scheduled is estimated based on the degree of exams in conflict. The degree determines the size of a segment in a roulette wheel, with a larger degree giving a larger segment. The roulette wheel selection mechanism selects an exam if the generated random number falls within the exam's segment. This overcomes the problem of repeatedly choosing and scheduling the same sequence of exams. We utilise the proposed Roulette Wheel Graph Colouring heuristic on the uncapacitated Carter's benchmark datasets. Results showed that this simple heuristic is capable of producing feasible solutions for all 13 instances.
Asian Journal of Applied Sciences, 2014
2010 10th International Conference on Intelligent Systems Design and Applications, 2010
... 535-550, 2000. [13] S. Abdullah, K. Shaker, B ... [15] M. Chiarandini, M. Birattari, K. Socha... more ... 535-550, 2000. [13] S. Abdullah, K. Shaker, B ... [15] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria, An effective hybrid algorithm for university course Timetabling. Proceeding in Journal of Scheduling, Volume 9, Number 5 / October, 2006, Springer Netherlands, pp. ...
2010 International Symposium on Information Technology, 2010
... total number of students), as shown in equation (1) by Rossi Doria et al. [21]: ... Among the... more ... total number of students), as shown in equation (1) by Rossi Doria et al. [21]: ... Among these approaches, Turabieh and Abdullah [24] (Ll), which applied Tabu Based Memetic have outperformed many other approaches in the literature (with regards to Socha benchmark datasets). ...
Procedia - Social and Behavioral Sciences, 2011
Previously, human schedulers at the Universiti Kebangsaan Malaysia (UKM) were human decision make... more Previously, human schedulers at the Universiti Kebangsaan Malaysia (UKM) were human decision makers (BPA officer) who applied assignment procedure based on their experience with a little guidance from computer software to generate the exam timetable. They would take into account spreading exams evenly, and fairly, throughout the timetable but the size and complexity of the problem makes this unrealistic to be solved manually. Therefore, we have proposed a new extended graph colouring heuristics and developed a prototype to solve UKM examination timetabling problem, which has been practically used starting from Semester II, 2006/2007. The proposed work aims to produce an intelligent commercial scheduler that capable of producing a high quality examination timetable. We will utilise a new objective function that was proposed in our previous work to evaluate the quality of the timetable. The objective function considers both timeslots and days in assigning exams to timeslots, where a higher priority is given to minimise students having consecutive exams on the same day. The objective also tries to spread exams throughout the examination period. The outcome of the research could directly enhance services given by Bahagian Pengurusan Akademik (BPA), where BPA can produce a high quality exam timetable in a shorter time frame. Furthermore, this work might lead to reduce examination stress among students and might help them to obtain a better result by allowing ample revision time.
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically ... more ABSTRACT The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically explore the neighborhood of current solution using a set of predefined neighborhood structures. Since different problem instances have different landscape and complexity, the choice of which neighborhood structure to be applied is a challenging task. Different neighborhood structures may lead to different solution space. Therefore, this work proposes a learning mechanism in a Variable Neighborhood Search (VNS), refer to hereafter as a Variable Neighborhood Guided Search (VNGS). Its effectiveness is illustrated by solving a course timetabling problems. The learning mechanism memorizes which neighborhood structure could effectively solve a specific soft constraint violations and used it to guide the selection of neighborhood structure to enhance the quality of a best solution. The performance of the VNGS is tested over Socha course timetabling dataset. Results demonstrate that the performance of the VNGS is comparable with the results of the other VNS variants and outperformed others in some instances. This demonstrates the effectiveness of applying a learning mechanism in a VNS algorithm.
2011 3rd Conference on Data Mining and Optimization (DMO), 2011
... Whereas, Abdullah & Turabieh (2009) in R2 outperformed other approaches on medium4 da... more ... Whereas, Abdullah & Turabieh (2009) in R2 outperformed other approaches on medium4 dataset. ... [11] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria, An effective hybrid algorithm for university course Timetabling, proceeding in Journal of Scheduling, Volume 9 ...
2009 2nd Conference on Data Mining and Optimization, 2009
Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to inc... more Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to increase the level of generality in which optimization methodologies can operate. In this work, we propose a scatter search based hyper-heuristic (SS-HH) approach for solving examination timetabling problems. The scatter search operates at high level of abstraction which intelligently evolves a sequence of low level heuristics to use for a given problem. Each low level heuristic represents a single neighborhood structure. We test our proposed approach on the un-capacitated Carter benchmarks datasets. Experimental results show the proposed SS-HH is capable of producing good quality solutions which are comparable to other hyper-heuristics approaches (with regarding to Carter benchmark datasets).
2012 IEEE International Conference on Power and Energy (PECon), 2012
ABSTRACT
Journal of Applied Sciences, 2013
Journal of Applied Sciences, 2013
Journal of Applied Sciences, 2013
Information Sciences, 2013
Harmony Search Algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solution... more Harmony Search Algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The Nurse Rostering Problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature.
IEEE Transactions on Evolutionary Computation, 2000
ABSTRACT Abstract—Designing generic problem solvers that perform well across a diverse set of pro... more ABSTRACT Abstract—Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g. different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems; exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state of the art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies which have reported best known results for some instances in both problem domains.
According to Tucker (1982), in a span of 25 years after the second world war, various technologic... more According to Tucker (1982), in a span of 25 years after the second world war, various technological media have been used in teaching and learning at institutions of higher learning such as television, video tapes, slides, audio tapes, including computers. At the end of 1980s until ...
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT Multi parent crossover has been successfully applied to solve many combinatorial optimiz... more ABSTRACT Multi parent crossover has been successfully applied to solve many combinatorial optimization problems such as unconstrained binary quadratic programming problem (UBQP). This because using more than two parents has increased the intensification process by exploiting the information shared by multi parents. However not all type of crossovers are suitable to solve vehicle routing problem (VRP). Therefore, this work introduces a multi parent insertion crossover in solving vehicle routing problem with time windows (VRPTW) by enhancing two parent insertion crossovers. This crossover exchange information among three parents instead of two. Result tested on Solomon VRPTW benchmarks demonstrate that multi parent crossover outperformed two parent crossover on same instances. This prove the effectiveness of having more parents for crossover that can be help the search to find better quality solution.
This work presents a simple graph based heuristic that employs a roulette wheel selection mechani... more This work presents a simple graph based heuristic that employs a roulette wheel selection mechanism for solving examination timetabling problems. We arrange exams in non-increasing order of the number of conflicts (degree) that they have with other exams. The difficulty of each exam to be scheduled is estimated based on the degree of exams in conflict. The degree determines the size of a segment in a roulette wheel, with a larger degree giving a larger segment. The roulette wheel selection mechanism selects an exam if the generated random number falls within the exam's segment. This overcomes the problem of repeatedly choosing and scheduling the same sequence of exams. We utilise the proposed Roulette Wheel Graph Colouring heuristic on the uncapacitated Carter's benchmark datasets. Results showed that this simple heuristic is capable of producing feasible solutions for all 13 instances.
Asian Journal of Applied Sciences, 2014
2010 10th International Conference on Intelligent Systems Design and Applications, 2010
... 535-550, 2000. [13] S. Abdullah, K. Shaker, B ... [15] M. Chiarandini, M. Birattari, K. Socha... more ... 535-550, 2000. [13] S. Abdullah, K. Shaker, B ... [15] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria, An effective hybrid algorithm for university course Timetabling. Proceeding in Journal of Scheduling, Volume 9, Number 5 / October, 2006, Springer Netherlands, pp. ...
2010 International Symposium on Information Technology, 2010
... total number of students), as shown in equation (1) by Rossi Doria et al. [21]: ... Among the... more ... total number of students), as shown in equation (1) by Rossi Doria et al. [21]: ... Among these approaches, Turabieh and Abdullah [24] (Ll), which applied Tabu Based Memetic have outperformed many other approaches in the literature (with regards to Socha benchmark datasets). ...
Procedia - Social and Behavioral Sciences, 2011
Previously, human schedulers at the Universiti Kebangsaan Malaysia (UKM) were human decision make... more Previously, human schedulers at the Universiti Kebangsaan Malaysia (UKM) were human decision makers (BPA officer) who applied assignment procedure based on their experience with a little guidance from computer software to generate the exam timetable. They would take into account spreading exams evenly, and fairly, throughout the timetable but the size and complexity of the problem makes this unrealistic to be solved manually. Therefore, we have proposed a new extended graph colouring heuristics and developed a prototype to solve UKM examination timetabling problem, which has been practically used starting from Semester II, 2006/2007. The proposed work aims to produce an intelligent commercial scheduler that capable of producing a high quality examination timetable. We will utilise a new objective function that was proposed in our previous work to evaluate the quality of the timetable. The objective function considers both timeslots and days in assigning exams to timeslots, where a higher priority is given to minimise students having consecutive exams on the same day. The objective also tries to spread exams throughout the examination period. The outcome of the research could directly enhance services given by Bahagian Pengurusan Akademik (BPA), where BPA can produce a high quality exam timetable in a shorter time frame. Furthermore, this work might lead to reduce examination stress among students and might help them to obtain a better result by allowing ample revision time.
2012 4th Conference on Data Mining and Optimization (DMO), 2012
ABSTRACT The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically ... more ABSTRACT The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically explore the neighborhood of current solution using a set of predefined neighborhood structures. Since different problem instances have different landscape and complexity, the choice of which neighborhood structure to be applied is a challenging task. Different neighborhood structures may lead to different solution space. Therefore, this work proposes a learning mechanism in a Variable Neighborhood Search (VNS), refer to hereafter as a Variable Neighborhood Guided Search (VNGS). Its effectiveness is illustrated by solving a course timetabling problems. The learning mechanism memorizes which neighborhood structure could effectively solve a specific soft constraint violations and used it to guide the selection of neighborhood structure to enhance the quality of a best solution. The performance of the VNGS is tested over Socha course timetabling dataset. Results demonstrate that the performance of the VNGS is comparable with the results of the other VNS variants and outperformed others in some instances. This demonstrates the effectiveness of applying a learning mechanism in a VNS algorithm.
2011 3rd Conference on Data Mining and Optimization (DMO), 2011
... Whereas, Abdullah & Turabieh (2009) in R2 outperformed other approaches on medium4 da... more ... Whereas, Abdullah & Turabieh (2009) in R2 outperformed other approaches on medium4 dataset. ... [11] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria, An effective hybrid algorithm for university course Timetabling, proceeding in Journal of Scheduling, Volume 9 ...
2009 2nd Conference on Data Mining and Optimization, 2009
Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to inc... more Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to increase the level of generality in which optimization methodologies can operate. In this work, we propose a scatter search based hyper-heuristic (SS-HH) approach for solving examination timetabling problems. The scatter search operates at high level of abstraction which intelligently evolves a sequence of low level heuristics to use for a given problem. Each low level heuristic represents a single neighborhood structure. We test our proposed approach on the un-capacitated Carter benchmarks datasets. Experimental results show the proposed SS-HH is capable of producing good quality solutions which are comparable to other hyper-heuristics approaches (with regarding to Carter benchmark datasets).
2012 IEEE International Conference on Power and Energy (PECon), 2012
ABSTRACT
Journal of Applied Sciences, 2013
Journal of Applied Sciences, 2013
Journal of Applied Sciences, 2013
Information Sciences, 2013
Harmony Search Algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solution... more Harmony Search Algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The Nurse Rostering Problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature.
IEEE Transactions on Evolutionary Computation, 2000
ABSTRACT Abstract—Designing generic problem solvers that perform well across a diverse set of pro... more ABSTRACT Abstract—Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g. different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems; exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state of the art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies which have reported best known results for some instances in both problem domains.