Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm

Author

Rayandra Yala Pratama, Wayan Firdaus Mahmudy

Abstract

Food distribution process is very important task because the product can expire during distribution and the further the distance the greater the cost. Determining the route will be more difficult if all customers have their own time to be visited. This problem is known as the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW problems can be solved using genetic algorithms because genetic algorithms generate multiple solutions at once. Genetic algorithms generate chromosomes from serial numbers that represent the customer number to visit. These chromosomes are used in the calculation process together with other genetic operators such as population size, number of generations, crossover and mutation rate. The results show that the best population size is 300, 3,000 generations, the combination of crossover and mutation rate is 0.4:0.6 and the best selection method is elitist selection. Using a data test, the best parameters give a good solution that minimize the distribution route.

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References


Mahmudy, Wayan Firdaus, Improved Simulated Annealing For Optimization Of Vehicle Routing Problem With Time Windows (VRPTW), 2014. Kursor Journal, vol. 7, no. 3, page 109-116.

Puljic, K., Manger, R., A Distributed Evolutionary Algorithm With A Superlinear Speedup For Solving The Vehicle Routing Problem, 2012. Computing and informatics.

Li, P. dkk, Vehicle Routing Problem with Soft Time Windows Based on Improved Genetic Algorithm for Fruits and Vegetables Distribution, 2015. Hindawi Publishing Corporation.

Nazif, H., Lee, L.S, Optimized Crossover Genetic Algorithm for Vehicle Routing Problem with Time Windows, 2010. American Journal of Applied Sciences.

Saputri, M.W, Mahmudy, W.F & Ratnawati, D.E, “Optimasi Vehicle Routing Problem with Time Window (VRPTW) Menggunakan Algoritma Genetika Pada Distribusi Barang”, 2015. Jurnal Skripsi Mahasiswa PTIIK Universitas Brawijaya.

Mahmudy, W.F, “Dasar-Dasar Algoritma Evolusi”, 2015. Program Teknologi Informasi dan Ilmu Komputer, Universitas Brawijaya, Malang.

Srivastava, P.R, Kim, T., Application of Genetic Algorithm in Software Testing, 2009. International Journal of Software Engineering and Its Applicantions, Vol. 3 No. 4.

Sundarningsih, D., Mahmudy, W.F, & Sutrisno, “Penerapan Algoritma Genetika Untuk Optimasi Vehicle Routing Problem With Time Window (VRPTW) : Studi Kasus Air Minum Kemasan”, 2015. Jurnal Skripsi Mahasiswa PTIIK Universitas Brawijaya.

Gandhi, S., Khan, D., & Solanki, V.S, A Comparative Analysis of Selection Scheme, 2012. International Journal od Soft Computing and Engineering (IJSCE).

Hlaing, Z.C.S.S, Khine, M.A., Solving Traveling Salesman Problem by Using Improved And Colony Optimization Algorithm, 2011. International Journal of Information and Education Technology.

Kumar, A., Encoding Schemes in Genetic Algorithm, 2013. International Journal of Advanced Research in IT and Engineering.

Soni, N., Kumar, Dr. T., Study of Various Mutation Operators in Genetic Algorithms, 2014. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4519-4521.




DOI: http://dx.doi.org/10.25126/jitecs.20172216