Combinatorial Bees Algorithm for vehicle routing problem

Asrul Harun Ismail; Natalia Hartono; Sultan Zeybek; Mario Caterino; Kaiwen Jiang

doi:10.1002/masy.202000284

Combinatorial Bees Algorithm for vehicle routing problem

Asrul Harun Ismail, Natalia Hartono, Sultan Zeybek, Mario Caterino, Kaiwen Jiang

Research output: Contribution to journal › Article › peer-review

Abstract

Vehicle routing problem (VRP) is a combinatorial optimization problem that has been studied intensively for years. Although VRP can be solved using an exact method for small instances, a more complex one will be impractical due to the nature of the problem as an NP‐hard problem. A metaheuristic is more suitable to solve this kind of problem because the near‐optimal solution can be found in a relatively shorter time compared to the exact algorithm. Bees Algorithm (BA) as nature‐inspired metaheuristic is used to find a near‐optimal solution of VRP‐TSPLIB's datasets. This study aims to provide the results of BA on the standard dataset of VRP. The BA has a very good performance, with 3.9% of the average Best‐Error and 1.2 million of the average evaluations to reach the solution.

Original language	English
Article number	2000284
Number of pages	4
Journal	Macromolecular Symposia
Volume	396
Issue number	1
DOIs	https://doi.org/10.1002/masy.202000284
Publication status	Published - 16 Apr 2021

Keywords

bees algorithm
combinational optimization problem
vehicle routing problem

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10.1002/masy.202000284

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title = "Combinatorial Bees Algorithm for vehicle routing problem",

abstract = "Vehicle routing problem (VRP) is a combinatorial optimization problem that has been studied intensively for years. Although VRP can be solved using an exact method for small instances, a more complex one will be impractical due to the nature of the problem as an NP‐hard problem. A metaheuristic is more suitable to solve this kind of problem because the near‐optimal solution can be found in a relatively shorter time compared to the exact algorithm. Bees Algorithm (BA) as nature‐inspired metaheuristic is used to find a near‐optimal solution of VRP‐TSPLIB's datasets. This study aims to provide the results of BA on the standard dataset of VRP. The BA has a very good performance, with 3.9% of the average Best‐Error and 1.2 million of the average evaluations to reach the solution.",

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doi = "10.1002/masy.202000284",

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T1 - Combinatorial Bees Algorithm for vehicle routing problem

AU - Ismail, Asrul Harun

AU - Hartono, Natalia

AU - Zeybek, Sultan

AU - Caterino, Mario

AU - Jiang, Kaiwen

PY - 2021/4/16

Y1 - 2021/4/16

N2 - Vehicle routing problem (VRP) is a combinatorial optimization problem that has been studied intensively for years. Although VRP can be solved using an exact method for small instances, a more complex one will be impractical due to the nature of the problem as an NP‐hard problem. A metaheuristic is more suitable to solve this kind of problem because the near‐optimal solution can be found in a relatively shorter time compared to the exact algorithm. Bees Algorithm (BA) as nature‐inspired metaheuristic is used to find a near‐optimal solution of VRP‐TSPLIB's datasets. This study aims to provide the results of BA on the standard dataset of VRP. The BA has a very good performance, with 3.9% of the average Best‐Error and 1.2 million of the average evaluations to reach the solution.

AB - Vehicle routing problem (VRP) is a combinatorial optimization problem that has been studied intensively for years. Although VRP can be solved using an exact method for small instances, a more complex one will be impractical due to the nature of the problem as an NP‐hard problem. A metaheuristic is more suitable to solve this kind of problem because the near‐optimal solution can be found in a relatively shorter time compared to the exact algorithm. Bees Algorithm (BA) as nature‐inspired metaheuristic is used to find a near‐optimal solution of VRP‐TSPLIB's datasets. This study aims to provide the results of BA on the standard dataset of VRP. The BA has a very good performance, with 3.9% of the average Best‐Error and 1.2 million of the average evaluations to reach the solution.

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DO - 10.1002/masy.202000284

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SN - 1022-1360

VL - 396

JO - Macromolecular Symposia

JF - Macromolecular Symposia

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M1 - 2000284

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Combinatorial Bees Algorithm for vehicle routing problem

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Keywords

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