Genetic Algorithm Approach to Logistics Transportation and Distribution Problems: A Case Study of Parcel Delivery Services

Authors

  • Miftahol Arifin Institut Teknologi Telkom Purwokerto

DOI:

https://doi.org/10.31315/opsi.v14i2.4903

Keywords:

Genetic Algorithm, Transportation, Logistik, Chromosome

Abstract

Transportation and distribution activities require companies to have careful transportation planning to maintain resource efficiency. One form of planning in the transportation is planning the route and the number of vehicles. The research output is to get the best shipping route from each transportation in the form of the smallest shipping cost, delivery distance and shortest time. The approach used to determine the best route is the genetic algorithm (GA) method. In the application of GA in finding routes according to the objective, it gives the best results. From the results of the study, it was concluded that the GA algorithm was able to produce the best route effectiveness of 35.5% with a minimum distance of 76.4 km with the route 1-16-7-14-13-17-21-22-6-19-18 - 3-23-9-19-15-24-4-15-20-8-11-2-25-5-2 and a travel time of 4 hours 51 minutes and a fee of Rp. 213,000 with a delivery route on the test.

References

Berlianty, I. (2010), Teknik-Teknik Optimasi Heuristik, Graha Ilmu

Cheng, R., Gen, M. (2006), A tutorial survey of job-shop scheduling problems using genetic algorithm – I. Representation”, Computers and Industrial Engineering, 30(4), pp. 983-997.

Deb, K. (2011), “Multi-objective optimization using evolutionary algorithms”, New York: John Wiley & Sons.

Deb, K. and Gulati, S. (2010), “Design of truss-structures for minimum weight using genetic algorithms”, Finite Elements in Analysis and Design, 37, pp. 447-465.

Goldberg, D. E. (1989), “Genetic algorithms in search optimization & learning”, Addison-Wesley.

Jenkins, W. (2001), “Structural optimization with the genetic algorithm”, Structural engineering, 69, pp. 418-422.

Michalewicz, Z., Dasgupta, D., and Le Riche, R. G. (2010), “Schoenauer M. Evolutionary algorithms for constrained engineering problems”, Computers Industrial Engineering, 30(4), pp. 851-870.

Narayanan, S. (2008), “On improving multiobjective genetic algorithms for design optimization”, Structural Optimization, 18(2-3), pp. 146-155.

Osyczka, A. (2002), “Evolutionary algorithms for single and multicriteria Design optimization”, Germany: Physica Verlag.

Wu, S. J. and Chow, P. T. (2005), “Steady-state genetic algorithms for discrete optimization of trusses”, Computers & structures, 56, pp. 979-991.

Xie, Y. M. and Steven, G. P. (2003). “A simple evolutionary procedure for structural optimization”, Computers & structures, 49, pp. 885-896.

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Published

2021-12-21

Issue

Vol. 14 No. 2 (2021): ISSN 1693-2102

Section

Artikel

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