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.
Currently, the issue of the fuel crisis has become a global concern. The distribution sector is one of the sectors that consume the most significant fuel. Therefore, an effective procedure for fuel energy efficiency is needed to resolve the routing problem. In addition, the vehicle load must be considered in delivery and pickup at each node. This research proposes the novel Hybrid Yellow Saddle Goatfish Algorithm (HYSGA) algorithm to solve the Fuel Consumption Vehicle Routing Problem Simultaneous Pickup and Delivery (FCVRPSPD) problem. The objective function to be achieved was to minimize fuel costs. This study conducted experiments with HYSGA parameters such as the number of Goatfish, iterations, and the number of goatfish clusters to optimize the FCVRPSPD problem. In addition, a sensitivity analysis was presented to examine the effect of the FCVRPSPD variable on fuel costs. This study also compared the proposed algorithm with several state-of-the-art procedures. The results showed that the parameters of the number of Goatfish and the HYSGA iteration affected fuel costs. Furthermore, based on experiments, the proposed algorithm provided a competitive fuel cost compared to other algorithms.
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