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CHAPTER 1 INTRODUCTION 1.1 Problem Statement and Background 1.1.1 Problem Statement Course scheduling problems are very common, but very difficult to solve in practice. They are known as constraint optimization problems, NP hard problems, these are concerned with the allocations, subject to constraints of given resources to objects in space and time in such a way as to satisfy a possible set of desirable objectives [1, 2, 3]. Courses will be scheduled to time and classrooms so that lecturers can teach and students can attend these courses without any conflicts. A large number of researches have been carried out on these problems [1, 2, 3]. However, most of the researches have focused on solving the problems of universities without the separation of resources between faculties. The course scheduling for a multiple faculty university still needs more researches [4, 5]. Faculty 1 Lecturers Classrooms Courses Timetable Faculty 2 Lecturers Classrooms Courses Timetable Shared lecturers, courses, and classrooms Faculty n Lecturers Classrooms Courses Timetable FIGURE 1-1 Shared lecturers, courses, and classrooms The course scheduling will become more complex in a multiple faculty university where each faculty has its own resources such as lecturers, courses, and classrooms, as illustrated in Figure 1-1. Moreover, these resources can be shared between faculties. The lecturers working in a faculty can teach courses of other faculties. The courses can be attended by students who come from different faculties.
2 The classrooms are sometime shared between faculties. Each faculty needs its own timetable for its own resources. As a result, many problems still exist in the course scheduling related to the shared resources. Course scheduling itself contains a large number of conflicts and needs a large amount of processing time. For course scheduling in the multiple faculties, the data used for scheduling also needs to be collected and shared across the faculties. This study proposes a hybrid centralized and de-centralized approach, genetic algorithm, and grid computing environment to the course scheduling problem in multiple faculty universities. The proposed approach and the genetic algorithm are used to solve the NP hard problems. In addition, the grid computing environment is used as infrastructure for distributed and parallel computing. 1.1.2 Background The genetic algorithm (GA) is a global search optimization algorithm using parallel points. While searching for solutions, the GA uses a fitness function that affects the direction of the search [6]. The GA evaluates the population by using genetic operators such as selection, crossover, and mutation. The outline of the basic GA is presented in Figure 1-2. 1 [Start] Generate random population of n chromosomes. 2 [Fitness] Evaluate the fitness f(x) of each chromosome x in the population. 3 [New population] Create a new population by repeating following steps until the new population is complete. 3.1 [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected). 3.2 [Crossover] With a crossover rate cross over the parents to form new offspring (children). If no crossover was performed, offspring is the exact copy of parents. 3.3 [Mutation] With a mutation rate mutate new offspring at each locus (position in chromosome). 3.4 [Accepting] Place new offspring in the new population. 4 [Replace] Use new generated population for a further run of the algorithm. 5 [Test] If the end condition is satisfied, stop, and return the best solution in current population. 6 [Loop] Go to step 2. FIGURE 1-2 Outline of the basic genetic algorithm [6]
Page 1 and 2: COURSE SCHEDULING IN MULTIPLE FACUL
Page 3 and 4: ชื่อ : นายฮูเ
Page 5 and 6: TABLE OF CONTENTS Page Abstract (in
Page 7 and 8: LIST OF TABLES Table Page 2-1 Cours
Page 9: LIST OF FIGURES (CONTINUED) Figure
Page 13 and 14: 4 Classroom i Time-slot Hour Mon Tu
Page 15 and 16: CHAPTER 2 LITERATURE REVIEW In this
Page 17 and 18: 9 teach effectively. However, in so
Page 19 and 20: 11 The set of vertexes are consider
Page 21 and 22: 13 Gueret et al. have implemented a
Page 23 and 24: 15 When the atoms are at a high tem
Page 25 and 26: 17 The outline of the basic genetic
Page 27 and 28: 19 As shown in Figure 2-6, first of
Page 29 and 30: 21 2.3.2.1 Crossover After we have
Page 31 and 32: 23 these chromosomes. According to
Page 33 and 34: 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
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54 GIIS from the client machine m1.
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56 The complete source code for man
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58 3.8.2.2 Stage 2 After the centra
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60 3.8.3.3 The Gatekeeper The gatek
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62 TABLE 4-1 (CONTINUED) Class Seme
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64 26 lecturers are assigned to tea
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66 TABLE 4-2 (CONTINUED) Course Sec
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68 This result shows that the GA ra
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70 should revise the chromosome rep
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72 - Population size : 10 - Crossov
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74 for network communication is muc
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76 TABLE 4-5 Timetable created by t
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CHAPTER 5 CONCLUSION 5.1 Conclusion
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REFERENCES 1. Alkan, A. and Ozcan,
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83 20. Burke, E. K. and Newall, J.
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85 38. Globus Toolkit [Online]. Ava
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88 This section presents the struct
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90 A.5 Building TABLE A-5 Building
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92 A.10 Program TABLE A-10 Program
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94 A.14 Timetable TABLE A-14 Timeta
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96 This section presents in detail
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98 TABLE B-2 Group, user ID and pas
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100 broadcastdelay 0.008 authentica
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102 mycapw [enter] During the above
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104 service gsigatekeeper { socket_
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106 B.3.3 Starting the MDS on All o
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108 B.4 Checking the Installation T
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110 This section introduces the ste
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112 C.3.2 Configuration This sectio
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114 [root@m1 root]#grid-proxy-info
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116 The system will automatically p
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118 C.6 Installing JDBC Driver on L
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120 This section presents how to co
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122 All the following files are com
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124 } } } try { ctx.close(); } catc
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126 }//for }else if(attribute.equal
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128 public static void main(String
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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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