Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Fajar Delli Wihartiko, Hagni Wijayanti, Fitria Virgantari

Judul : Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem
Abstrak :

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems
with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO)
algorithm. Both algorithms have different advantages and disadvantages when applied to the
case of optimization of the Model Integer Programming for Bus Timetabling Problem
(MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted
with various constraints. The comparison results show that the PSO algorithm is superior in
terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Tahun : 2018 Media Publikasi : Seminar Internasional
Kategori : Prosiding No/Vol/Tahun : 332 / 2018 / 2018
ISSN/ISBN : 10.1088/1757-899X/332/1/012020
PTN/S : Universitas Pakuan Program Studi : ILMU KOMPUTER
Bibliography :

[1] Wihartiko FD, Buono A and Silalahi BP, Integer programming model for optimizing bus
timetable using genetic algorithm. IOP Conference Series: Materials Science and Engineering.
166(1) 1-9 (2017)
[2] Genova K and Guliashki V, Linear Integer Programming Methods and Approaches – A Survey,
Cybernetics and Information Technologies 11(1) 3-25 (2011)
[3] Huy PNA, San CTB and Triantaphyllou E. Solving Integer Programming Problems Using
Genetic Algorithms. ICEIC. Ha-Noi. (2004).
[4] E.C. Laskari, K.E. Parsopoulos and M.N. Vrahatis, Particle Swarm Optimization for Integer
Programming. IEEE. (2002).
[5] Chuanjiao S, Wei Z, Yuanqing W. Scheduling Combination and Headway Optimization of BusRapid Transit. Journal of Transportation Systems Engineering and Information Technology.
2008; 8(5): 61–67
[6] Kidwai FA. A Genetic Algorithm Based Bus Schedulling Model for Transit Network. Eastern Asia
Society for Transportation Studies. Bangkok. 2005; 05: 477–489
[7] Zhang M. The Research on Multi-objective School Bus Vehicle Routing Problems Based On Bilevel
Programming. Chengdu, China: Southwest Jiaotong University; 2008.
[8] Ben Sghaier S, Ben Guedria N, Mraihi R, editors. Solving School Bus Routing Problem with
genetic algorithm. Advanced Logistics and Transport (ICALT), 2013 International
Conference on; 2013: IEEE. pp. 7–12.
[9] Minocha B, Tripathi S, editors. Solving School Bus Routing Problem Using Hybrid Genetic
Algorithm: A Case Study. Proceedings of the Second International Conference on Soft
Computing for Problem Solving (SocProS 2012), December 28–30, 2012; 2014: Springer. pp.
[10] Zhang Y, Wang S, and Ji1 G, A Comprehensive Survey on Particle Swarm Optimization
Algorithm and Its Applications. Hindawi Publishing Corporation Volume 2015, Article ID
931256, 38 page
[11] Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A Comprehensive Review of Swarm
Optimization Algorithms. PLoS ONE 10(5): e0122827. doi:10.1371/journal.pone.0122827