Implementation of Genetic Algorithm to academic scheduling system

Hariyadi, Hanny Prastya; Widiyaningtyas, Triyanna; Arifin, Muhammad; Sendari, Siti

doi:10.1109/tencon.2016.7848378

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…GA is a popular solution method and is often used to optimize solutions to solve problems in many fields such engineering, computer sciences, economic management and supply chain management [9]. GA's advantages over other algorithms is that GA solves problems by utilizing optimum solution clustering so algorithm is less opportunity to be trapped in local optimum [10]. Other than that, compare to other algorithm, GA is more simple and very direct in providing the solutions [8].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

Zakuan¹,

Abdul-Rahman²,

Jantan³

2018

IJET

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Succession planning is a subset of talent management that deals with multi-criteria and uncertainties which are quite complicated, ambiguous, fuzzy and troublesome. Besides that, the successor selection involves the process of searching the best candidate for a successor for an optimal selection decision. In an academic scenario, the quality of academic staff contributes to achieving goals and improving the performance of the university at the international level. The process of selecting appropriate academic staff requires good criteria in decision-making. The best candidate's position and criteria for the selection of academic staff is the responsibility of the Human Resource Management (HRM) to select the most suitable candidate for the required position. The various criteria that are involved in selecting academic staff includes research publication, teaching skills, personality, reputation and financial performance. Previously, most studies on multi-criteria decision-making adopt Fuzzy Analytical Hierarchy Process (FAHP). However, this method is more complex because it involved many steps and formula and may not produce the optimum results. Therefore, Genetic Algorithm (GA) is proposed in this research to address this problem in which a fitness function for the successor selection is based on the highest fitness value of each chromosome.

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Section: Genetic Algorithmmentioning

confidence: 99%

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

Zakuan¹,

Abdul-Rahman²,

Jantan³

2018

IJET

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“…Using GA in scheduling was reported that it can produce an optimal schedule [2][3][4][5][6][7][8][9][10][11][12]. GA can be used in a complete operation step [2,6,7,9,11,12], in a modification operation step e.g. eliminate crossover [3,8] or in combination operation step with another computation method [5,10].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm for lecturing schedule optimization

Kristiadi¹,

Hartanto²

2019

Indonesian J. Comput. Cybern. Syst.

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Scheduling is a classic problem in lecturing. Rooms, lecturers, times and scheduling constraints must be managed well to get an optimal schedule. University of Boyolali (UBY) also encounter the same scheduling problems. The problem was tried to be solved by building a library based on Genetic Algorithm (GA). GA is a computation method which inspired by natural selection. The computation consists of some operators i.e. Tournament Selection, Uniform Crossover, Weak Parent Replacement and two mutation operators (Interchanging Mutation and Violated Directed Mutation (VDM)). The two mutation method are compared to find which better mutation operator. The library was planned to have a capability to define custom constraints (scheduling requirements that were not accommodated by the library) without core program modifications. The test results show that VDM is more promising for optimal solutions than Interchanging Mutation. In UBY cases, optimal solution (fitness value=1) is reached in 12 minutes 41 second with adding 6 new room and inactivated 2 constraint i.e. lecturing begins at 14.00 except for 3rd semester of science law study program with morning class and lecturing participants must not over classroom capacity.

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“…Lecture scheduling is one of the most important parts supporting lecturing process. Some factors that must be considered in the scheduling of lectures are lecturers, rooms, and time [1]. In addition to these factors, in lecture scheduling should also be noted the suitability of the credits of the subjects with the length of time lectures.…”

Section: Introductionmentioning

confidence: 99%

“…Hariyadi et al, implements GA in academic scheduling. In the study, lecturer time has been considered [1]. In this research, besides the time of lecturers, the lecture credit and the Friday prayer time are also considered in the preparation of the lecture schedule.…”

Section: Introductionmentioning

confidence: 99%

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Lecture Scheduling Automation Using Genetic Algorithm

Herman¹,

Syafie²,

Lantara³

2019

IJEAT

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This paper aims produce an academic scheduling system using Genetic Algorithm (GA) to solve the academic schedule. Factors to consider in academic scheduling are the lecture to be held, the available room, the lecturers and the time of the lecturer, the suitability of the credits with the time of the lecture, and perhaps also the time of Friday prayers, and so forth. Genetic Algorithms can provide the best solution for some solutions in dealing with scheduling problems. Based on the test results, the resulting system can automate the scheduling of lectures properly. Determination of parameter values in Genetic Algorithm also gives effect in producing the solution of lecture schedule

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Implementation of Genetic Algorithm to academic scheduling system

Cited by 6 publications

References 4 publications

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

Genetic Algorithm for lecturing schedule optimization

Lecture Scheduling Automation Using Genetic Algorithm

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