Implementation of discrete particle swarm optimization algorithm in the capacitated vehicle routing problem

Authors

  • Aisyahna Nurul Mauliddina Department of Logistics Engineering, Pertamina University
  • Faris Ahmad Saifuddin Department of Logistics Engineering, Pertamina University
  • Adesatya Lentera Nagari Department of Logistics Engineering, Pertamina University
  • Anak Agung Ngurah Perwira Redi Department of Industrial Engineering-Binus Graduate Program, Binus University
  • Adji Candra Kurniawan Department of Logistics Engineering, Pertamina University
  • Nanda Ruswandi Department of Logistics Engineering, Pertamina University

DOI:

https://doi.org/10.30656/jsmi.v4i2.2607

Keywords:

Discrete particle swarm optimization, Capacitated vehicle routing problem, Repeated measure ANOVA, Metaheuristic

Abstract

Capacitated Vehicle Routing Problem (CVRP) is known as an NP-hard problem. It is because CVRP problems are very hard for finding optimal solutions, especially in large instances. In general, the NP-hard problem is difficult to solve in the exact method, so the metaheuristic approach is implemented in the CVRP problem to find a near-optimal solution in reasonable computational time. This research uses the DPSO algorithm for solving CVRP with ten instances of benchmark datasets. DPSO implementation uses tuning parameters with the One Factor at Time (OFAT) method to select the best DPSO parameters. The outcome objective function will be compared with several PSO models proposed in previous studies. Statistical test using One Way Reputed Measure ANOVA is needed to compare algorithm performance. First, ANOVA uses for comparing’s results. Then, ANOVA is also used to test DPSO’s performance compared with DPSO-SA, SR-1, and SR-2 algorithm. The computational result shows that the basic DPSO algorithm not competitive enough with other methods for solving CVRP.

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Published

2020-12-29

Issue

Vol. 4 No. 2 (2020)

Section

Research Article

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How to Cite

[1]
“Implementation of discrete particle swarm optimization algorithm in the capacitated vehicle routing problem”, j. sist. manaj. ind., vol. 4, no. 2, pp. 117–128, Dec. 2020, doi: 10.30656/jsmi.v4i2.2607.
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