Time-dependent green vehicle path optimization method with time window under fuzzy demand

Abstract

The invention provides a time-dependent green vehicle path optimization method with a time window under a fuzzy demand, and the method comprises the steps of determining a speed-time dependent function, and deducing a vehicle energy consumption calculation model through combining with the speed function; establishing a fuzzy opportunity constrained programming model based on a fuzzy credibility theory; formulating a rescheduling strategy, and formulating distribution plans again for the customers who fail in pre-optimization path service and subsequent customers; generating an initial population by adopting chaotic mapping, and embedding three neighborhood structures on the basis of a genetic algorithm; and introducing a self-adaptive neighborhood search frequency strategy, and designing a chaos genetic variable neighborhood algorithm to solve the established model, so that a final optimization scheme is obtained. According to the invention, the fuzziness of customer demands, the continuous change of the speed of a distribution area road network, the vehicle speed, the load, the road gradient and other influences on oil consumption are fully considered, the invention is closer to the reality of distribution production activities, and the research on the vehicle path problem is expanded and deepened.

Description

Technical field [0001] The present invention relates to the field of path optimization, and more particularly to the time-dependent green vehicle path optimization method of time and window with fuzzy demand. Background technique [0002] The optimization algorithm for vehicle path problems is mainly divided into accurate solution algorithms and intelligent algorithms. Accurate solving algorithms can accurately identify the global optimal solution, such as Djustra algorithm. However, as the scale of the algorithm increases, the operating time of the algorithm will increase sharply, and it is not suitable for solving large-scale problems. The intelligent algorithm will be subjected to a better area by intersection and local search and other operations, and finally obtain the solution that people can accept. At the same time, the intelligent algorithm is not subject to search spatial restriction hypothesis, and the global optimal solution can be found in high probability from the h...

AI Technical Summary

Problems solved by technology

[0005] According to the existing method proposed above, the fuel consumption cost and the impact of vehicle speed, vehicle load, road gradient, etc. on fuel consumption during the delivery process are not considered, resulting in technical problems that cannot meet the energy-saving requirements, and a time with a time window under fuzzy requirements is provided. Dependent Green Vehicle Routing Optimization Method

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