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80 centralized approach and grid computing environment. Now the centralized course scheduling program and decentralized course scheduling program are considered as jobs. These jobs are scheduled to be executed. The centralized course scheduling job is performed first, and then the decentralized course scheduling jobs are performed in parallel on separate machines. The decentralized course scheduling program must give results that do not conflict with the centralized course scheduling output. The use of the grid computing environment gave a high level of efficiency. It reduces significantly the overall execution time for a resultant solution. This is because a very large problem with many conflicted constraints is now separated into small size problems to be processed in parallel by many different machines instead of using only one machine. 5.2 Future Works Overall, our preliminary experiments suggested that the proposed model has been successful to satisfy the objectives in our proposal. We have worked on two interesting areas: the genetic algorithm and the grid computing. They are wide areas, so what has been obtained is a foundation for further research. Our experiments identified the GA parameters for an effective GA. Further experiments should be done for various data and more soft constraints. We also need design algorithms that are able to automatically identify suitable values for the GA parameters. Local search techniques should be used to improve the speed of the GA. The local search algorithms should also help the GA to create solutions that are able to minimize use of university resources, e.g. the number of used classrooms and the stretch of lecturer time. To satisfy both hard and soft constraints in a balanced way, the multi-objective genetic algorithm should be researched. The grid computing environment was implemented on Linux machines. For more flexible use, it should be developed for heterogeneous environments with more machines added.
REFERENCES 1. Alkan, A. and Ozcan, E. “Memetic Algorithms for Timetabling.” IEEE Congress on Evolutionary Computation. 3 (2003, December 8-12) : 1796-1802. 2. Marc Buf, Tim Fischer, et al. “Automated solution of a highly constrained school timetabling problem - preliminary results.” Applications of Evolutionary Computing : EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM, Como, Italy. (2001, April 18-20) : 431-440. 3 Goulas, G. and Housos, E. “SchedSP: Providing GRID-enabled Real - World Scheduling Solutions as Application Services.” EuroWeb 2002 Conference, St Anne's College, Oxford, UK. (2002, December 17-18). 4. Kaplansky, E., Kendall, G., et al. “Distributed Examination Timetabling.” PATAT '04 Proceedings of the 5th International Conference on the Practice and Theory of Automated Timetabling, Pittsburgh, PA USA. (2004, August 18-20) : 511-516. 5. Lim, A., Ang, J. C., et al. “UTTSExam: A Campus-Wide University Exam- Timetabling System”. Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, Edmonton, Alberta, Canada. (2002, July 28 - August 1) : 838-844. 6. Genetic Algorithm [Online]. Available from: http://cs.felk.cvut.cz/~xobitko/ga/gaintro.html [2005, May 2]. 7. Luis Ferreira, et al. Introduction to Grid Computing with Globus. IBM Redbooks, September 2003. 8. Bart Jacob, et al. Enabling Applications for Grid Computing with Globus. IBM Redbooks, June 2003. 9. Carter, M. W. and Laporte, G. “Recent Developments in Practical Course Timetabling.” In Edmund Burke and Michael Carter, editors, Practice and Theory of Automated Timetabling II, Springer-Verlag LNCS. 1408 (1998) : 3-19.
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COURSE SCHEDULING IN MULTIPLE FACUL
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ชื่อ : นายฮูเ
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TABLE OF CONTENTS Page Abstract (in
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LIST OF TABLES Table Page 2-1 Cours
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LIST OF FIGURES (CONTINUED) Figure
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2 The classrooms are sometime share
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4 Classroom i Time-slot Hour Mon Tu
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CHAPTER 2 LITERATURE REVIEW In this
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9 teach effectively. However, in so
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11 The set of vertexes are consider
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13 Gueret et al. have implemented a
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15 When the atoms are at a high tem
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17 The outline of the basic genetic
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19 As shown in Figure 2-6, first of
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21 2.3.2.1 Crossover After we have
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23 these chromosomes. According to
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25 2.4.1 Application Considerations
Page 35 and 36: 27 Southern California, the Argonne
Page 37 and 38: 29 2.4.3.2 Application Programming
Page 39 and 40: 31 2.5 Summary The course schedulin
Page 41 and 42: 34 3.1.5 Phase 5: Measurement a) To
Page 43 and 44: 36 All the above problems can be pr
Page 45 and 46: 38 Client at a faculty Client at th
Page 47 and 48: 40 The Grid system is only used by
Page 49 and 50: 42 3.6 The Proposed Genetic Algorit
Page 51 and 52: 44 Chromosome x i Fitness = f(x i )
Page 53 and 54: 46 A population is a list of n chro
Page 55 and 56: 48 Lecturers register their busy ti
Page 57 and 58: 50 A course is booked to the time-l
Page 59 and 60: 52 As mentioned in the previous sec
Page 61 and 62: 54 GIIS from the client machine m1.
Page 63 and 64: 56 The complete source code for man
Page 65 and 66: 58 3.8.2.2 Stage 2 After the centra
Page 67 and 68: 60 3.8.3.3 The Gatekeeper The gatek
Page 69 and 70: 62 TABLE 4-1 (CONTINUED) Class Seme
Page 71 and 72: 64 26 lecturers are assigned to tea
Page 73 and 74: 66 TABLE 4-2 (CONTINUED) Course Sec
Page 75 and 76: 68 This result shows that the GA ra
Page 77 and 78: 70 should revise the chromosome rep
Page 79 and 80: 72 - Population size : 10 - Crossov
Page 81 and 82: 74 for network communication is muc
Page 83 and 84: 76 TABLE 4-5 Timetable created by t
Page 85: CHAPTER 5 CONCLUSION 5.1 Conclusion
Page 89 and 90: 83 20. Burke, E. K. and Newall, J.
Page 91 and 92: 85 38. Globus Toolkit [Online]. Ava
Page 93 and 94: 88 This section presents the struct
Page 95 and 96: 90 A.5 Building TABLE A-5 Building
Page 97 and 98: 92 A.10 Program TABLE A-10 Program
Page 99 and 100: 94 A.14 Timetable TABLE A-14 Timeta
Page 101 and 102: 96 This section presents in detail
Page 103 and 104: 98 TABLE B-2 Group, user ID and pas
Page 105 and 106: 100 broadcastdelay 0.008 authentica
Page 107 and 108: 102 mycapw [enter] During the above
Page 109 and 110: 104 service gsigatekeeper { socket_
Page 111 and 112: 106 B.3.3 Starting the MDS on All o
Page 113 and 114: 108 B.4 Checking the Installation T
Page 115 and 116: 110 This section introduces the ste
Page 117 and 118: 112 C.3.2 Configuration This sectio
Page 119 and 120: 114 [root@m1 root]#grid-proxy-info
Page 121 and 122: 116 The system will automatically p
Page 123 and 124: 118 C.6 Installing JDBC Driver on L
Page 125 and 126: 120 This section presents how to co
Page 127 and 128: 122 All the following files are com
Page 129 and 130: 124 } } } try { ctx.close(); } catc
Page 131 and 132: 126 }//for }else if(attribute.equal
Page 133 and 134: 128 public static void main(String
Page 135 and 136: 130 System.out.println(CentralizedS
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132 gassJob[jobCount]=new GassJob(t
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134 /** * Init job out listeners fo
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136 // Start GASS server if (! star
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138 // waits for DONE or FAILED sta
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140 //get RSL of Job having index i
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142 class Job { private String jobn
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145 BIOGRAPHY Name : Mr. Nguyen Con
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