Mixed fleet based green clustered logistics problem under carbon emission cap

Sustainable transportation is an ever-demanding matter for cities and societies in light of minimizing global CO2 emissions. This paper introduces the mixed fleet based green clustered logistics problem (MFGCLP) under CO2 emission cap to deal with the sustainable development effort of the transportation industry. The mixed fleet consists of hydrogen vehicles and conventional vehicles. In the proposed distribution problem, customers are clustered in different segments based on similar characteristics. The customers belonging to a cluster must be served by the same vehicle before it visits customers from a different cluster or before it returns to the depot. The CO2 emission of the vehicles is realistically considered as a function of traveled distance, speed, and on-board cargo load. The problem also includes time windows for customers and maximum tour length for the routes. A new hybrid metaheuristic, combining particle swarm optimization (PSO) and neighborhood search, is proposed to solve the problem. Extensive computational experiments have been performed on newly generated problem instances, and benchmark problem instances adopted from the literature. The proposed hybrid PSO proved to be superior to the state-of-the-art algorithms available in the literature.

Automated summary

Sustainable transportation is essential for minimizing global CO2 emissions. This paper introduces a mixed fleet based green clustered logistics problem and proposes a new hybrid metaheuristic algorithm to solve it, which outperforms state-of-the-art algorithms in extensive computational experiments.