An Improved Genetic Algorithm for Vehicle Routing Problem Pick-up and Delivery with Time Windows

Authors

  • Muhammad Faisal Ibrahim Department of Logistics Engineering, Universitas Internasional Semen Indonesia, Indonesia
  • M.M Putri Logistics Engineering Department, Universitas Internasional Semen Indonesia
  • D Farista Logistics Engineering Department, Universitas Internasional Semen Indonesia
  • Dana Marsetiya Utama Industrial Engineering Department, Universitas Muhammadiyah Malang, Indonesia

DOI:

https://doi.org/10.22219/JTIUMM.Vol22.No1.1-17

Keywords:

VRPPDTW, Routing, Pickup and Delivery, Time Windows, Genetic Algorithm

Abstract

Vehicle Routing Problem (VRP) has many applications in real systems, especially in distribution and transportation. The optimal determination of vehicle routes impacts increasing economic interests. This research aims to find the optimal solution in Vehicle Routing Problem Pick-up and Delivery with Time Windows (VRPPDTW).  Targets of this problem included reducing distance travel and penalties. Three penalties that were considered are a capacity penalty, opening time capacity, and closing time capacity. An improved genetic algorithm was developed and used to determine the vehicle route.  There were one main depot and 42 customers. This research raised the problem of a shipping and logistics company. Analysis of the results showed that the proposed route obtained from improved genetic algorithms (GA) was better than the existing route and previous algorithm. Besides, this research was carried out an analysis on the effect of the number of iterations on distance traveled, the number of penalties, and the fitness value. This algorithm could be applied in VRPPDTW and produces an optimal solution.

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Published

2021-02-28

How to Cite

Ibrahim, M. F., Putri, M., Farista, D., & Utama, D. M. (2021). An Improved Genetic Algorithm for Vehicle Routing Problem Pick-up and Delivery with Time Windows. Jurnal Teknik Industri, 22(1), 1-17. https://doi.org/10.22219/JTIUMM.Vol22.No1.1-17

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