Allocation Of Thesis Supervisor Using Genetic Algorithm

Ratna Dewi Hidayaturrachmah, syaiful anam


Allocation of thesis supervisor is a way to determine the students’ thesis supervisor, so the students get the appropriate supervisor and the number of students guided by each supervisor does not exceed the maximum capacity. Allocation of thesis supervisor is not an easy taskbecause each supervisor has a capacity of the students that have to be guided and the supervisors major should be in accordance with the student's major in project.Allocation of thesis supervisor is an optimization problem that minimizes the number of mismatches between supervisor’s major and student’s project, and the number of supervisors whose guidance exceeds the maximum capacity.One of the methods that can be used to solve this problem is genetic algorithm.Genetic algorithm can solve optimization problems well on complex or even difficult mathematical models.The crossover operator is one of the operator that determines the success of the genetic algorithm.The commonly used crossover operator is the one cut point crossover operator, while after previous studies the position crossover operator is a crossover operator that developed to resolve the scheduling issues.Therefore, this research compares crossover position operator and the crossover one cut point operator to solve the problem of the thesis supervisor allocation. According to the results of the experiments, the genetic algorithm using crossover one cut point operators get better results in allocation of thesis supervisor rather than genetic algorithm with a position crossover operator.

Full Text:



N. K. Mawadda and F. M. Wayan, “Optimasi Penjadwalan Ujian Menggunakan Algoritma Genetika,” Kursor. Malang, 2006, Vol.2, pp. 1-8.

A. M. Chirkin, S. Z. Belloum, V. Kovalchuk and X. Make, “Executing time estimation for worklow scheduling,” Future Generation Computer System, Amsterdam, 2017, vol. 75, pp. 376-389.

L. Zuo, W. Changchun, L. Diao, J. Chen and Y. Huang, “Optimizing detailed schedules of a multiproduct pipeline by a monolithic MILP formulation,” Future Journal of Petroleum Science and Engineering, Beijing, 2017, vol. 150, pp. 148-163.

M. Guajardo and K. Jomsten, “The Stable Tournament: Matching Sports Schedules with Preferences,”Operations Research Letters, Bergen, 2017, vol. 45, pp. 461-466.

O. H. Salami and Y. E. Maman, “A Genetic Algorithm for Allocation Project Supervisors to Students,” I.J. Intelegent System and Aplications, Minna, 2016, vol. 10, pp. 51-59.

A. J. Umbakar and P. D. Shet, “Crossover operation in Genetic Algorithms: A Review,” ICTACT Journal on Soft Computing, Karad, 2015, vol. 06, pp. 1083-1092.

A. Kwanashie, R. W. Irving, D. F and C. T. Sing, “Profile-based optimal matchings in the student/project allocation problem,” In Combinatorial Algorithms, Springer International Publishing, 2014, pp. 213-225.

M. M. El-Sherbiny and Y. M. Ibrahim, “An artificial immune algorithm with alternative mutation methods: applied to the student project assignment problem,”in International Conference on Innovation and Information Management (ICIIM2012), Chengdu, vol. 13, pp. 123-134.

A. Toptal and I. Sabuncuoglu, “Coordination of Inbound and Outbond transportation schedules with the production schedule,” Computer and Industrial Engineering, Istambul, vol. 103, pp. 178-192.

D. G. Cattrysse and L. N. Wassenhove, “A survey of algorithms for the generalized assignment problem,”European Journal of Operational Research, Leuyun, 1992, vol. 60, pp. 260-272.

F. J. Correcher and R. V. Alvarez, “A biased random-key genetic Algoritm for the time-invariant berth allocation and quay crane assignment problem,”Expert Systems with Applications, Valencia, 2017, vol. 89, pp. 112-128.

H. King and P. Kim, “Reliability-redudancy allocation problem considering optimal redundancy strategy using paralel genetic algorithm,” Reliability Engineering and System Safety, Beijing. 2017, Vol. 159, pp. 153-160.

G. Mitchell, “Evolutionary Computation Applied to Combinatorial Optimisation Problems,” Shool of Electronic Engineering, Dublin City University, 2007.

L. Barbulescu, E. H. Adele, L. W. Darrell and M. Roberts, “Understanding Algorithm Performance on am Oversubscribed scheduling Aplication,” Colorado Journal of Artificial Intelegence Research, 2006, vol. 27, pp. 577-615.

Z. Zainudin, “AlgoritmaGenetika,”Andi, Yogyakarta, 2016


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.