Logistics vehicle low-carbon route planning method based on heuristic particle swarm optimization

Abstract

The invention discloses a logistics vehicle low-carbon route planning method based on a heuristic particle swarm algorithm, and the method comprises the following steps: (1), reading problem information, including the position coordinates and demand weight of a customer; (2) initializing algorithm parameters; (3) calculating the fitness of all individuals in the population, and determining an individual extreme value and a global extreme value; (4) performing variation on all individuals by adopting an individual multivariate variation strategy; (5) sequentially crossing the varied individuals with the individual extreme value and the global extreme value to generate new individuals; (6) updating the individual extreme value and the global extreme value; (7) carrying out local search on the individual extremum based on the heuristic information of priority unloading; (8) performing refined search on the global extremum based on the similarity of the population; and (9) judging whether a termination condition is met or not, if yes, terminating iteration, and outputting an individual with the optimal fitness which is the delivery service sequence of the trucks. The method has the advantages of high search speed, high search capability and low carbon emission in route planning.

Description

technical field [0001] The invention relates to the technical field of route planning, in particular to a low-carbon route planning method for logistics vehicles based on a heuristic particle swarm algorithm. Background technique [0002] As we all know, carbon dioxide (CO2) is the main factor causing the greenhouse effect. According to the US National Oceanic and Atmospheric Administration (NOAA), CO2 accounts for 63% of all greenhouse gases that cause global warming. According to the International Energy Agency (IEA), the increase in global carbon emissions in 2019 was about 33 billion tons. In addition to power generation and heat supply, the world's second largest contributor to carbon dioxide emissions is transportation, of which the carbon dioxide generated by transportation accounts for 23% of global carbon dioxide emissions, and the main source of total carbon emissions in transportation is road transportation. At present, most of the research on vehicle road transp...

AI Technical Summary

Problems solved by technology

The traditional particle swarm optimization algorithm solves the low-carbon route planning of logistics vehicles without using the characteristics of the problem, the search is extremely blind, and the convergence speed is slow, and the traditional particle swarm optimization algorithm has the disadvantages of being easy to fall into local optimum and low solution accuracy. In summary, a convergence algorithm is proposed. A route planning method with higher efficiency and strong ability to jump out of local optimum is extremely necessary

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