The issue of green logistics has received full attention from the government and business people. It is closely related to the increase in Green House Gas (GHG) by transportation activities in the logistics sector. Controlling fuel consumption in transportation activities is fundamental in dealing with GHG. Therefore, the Fuel Consumption Vehicle Routing Problem (FCVRP) is proposed as a solution model in optimizing fuel consumption in the logistics sector. This study aims to develop a Hybrid Particle Swarm Optimization (HPSO) algorithm to solve the FCVRP problem. The proposed algorithm is the development of the PSO algorithm with local procedures search. Several experiments were carried out to determine the HPSO parameter's effect on the minimization of fuel consumption at issue in the FCVRP. The results of the experiment show that HPSO is effective for optimizing fuel consumption in the FCVRP study.

The depletion of non-renewable fuel reserves is the biggest problem in the logistics sector. This problem encourages the transportation sector to increase fuel efficiency in distribution activities. The fuel optimization problem in distribution routing problems is called the Fuel Consumption Vehicle Routing Problem (FCVRP). This study proposes a novel Hybrid Henry Gas Solubility Optimization (HHGSO) to solve FCVRP problems. Experiments with several parameter variants were carried out to determine the performance of HHGSO in optimizing fuel consumption. The results show that the parameters of the HHGSO algorithm affect fuel consumption and computation time. In addition, the higher the KPL, the smaller the resulting fuel consumption. The proposed algorithm is also compared with several algorithms. The comparison results show that the proposed algorithm produces better computational time and fuel consumption than the Hybrid Particle Swarm Optimization and Tabu Search algorithms.