Simulated annealing algorithm for solving the capacitated vehicle routing problem: a case study of pharmaceutical distribution

  • Anak Agung Ngurah Perwira Redi Pertamina University
  • Fiki Rohmatul Maula Pertamina University
  • Fairuz Kumari Pertamina University
  • Natasha Utami Syaveyenda Pertamina University
  • Nanda Ruswandi Pertamina University
  • Annisa Uswatun Khasanah Universitas Islam Indonesia
  • Adji Chandra Kurniawan Pertamina University
Abstract views: 199 , PDF downloads: 120
Keywords: VRP, Nearest neighbour, Simulated annealing, Heuristic method

Abstract

This study aims to find a set of vehicles routes with the minimum total transportation time for pharmaceutical distribution at PT. XYZ in West Jakarta. The problem is modeled as the capacitated vehicle routing problem (CVRP). The CVRP is known as an NP-Hard problem. Therefore, a simulated annealing (SA) heuristic is proposed. First, the proposed SA performance is compared with the performance of the algorithm form previous studies to solve CVRP. It is shown that the proposed SA is useful in solving CVRP benchmark instances. Then, the SA algorithm is compared to a commonly used heuristic known as the nearest neighborhood heuristics for the case study dataset. The results show that the simulated Annealing and the nearest neighbor algorithm is performing well based on the percentage differences between each algorithm with the optimal solution are 0.03% and 5.50%, respectively. Thus, the simulated annealing algorithm provides a better result compared to the nearest neighbour algorithm. Furthermore, the proposed simulated annealing algorithm can find the solution as same as the exact method quite consistently. This study has shown that the simulated annealing algorithm provides an excellent solution quality for the problem.

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Published
2020-07-28
How to Cite
[1]
A. A. N. P. Redi, “Simulated annealing algorithm for solving the capacitated vehicle routing problem: a case study of pharmaceutical distribution”, j. sist. manaj. ind., vol. 4, no. 1, pp. 41-49, Jul. 2020.
Section
Research Article