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Vehicle logistics scheduling method and device based on multi-objective ant colony algorithm, storage medium and terminal

A scheduling method, ant colony algorithm technology, applied in logistics, computing, computing models, etc., can solve problems such as few variables to consider, non-optimal scheduling scheme, low utilization rate of transportation resources, etc., to improve system efficiency and avoid failure Scheduling effect

Active Publication Date: 2018-11-20
ANJI AUTOMOTIVE LOGISTICS +1
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Problems solved by technology

Such a vehicle logistics scheduling method has many disadvantages, such as few variables to consider, non-optimal scheduling plan, low utilization rate of transportation resources, and slow order response speed, which cannot meet the expectations of automakers and customers.

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  • Vehicle logistics scheduling method and device based on multi-objective ant colony algorithm, storage medium and terminal
  • Vehicle logistics scheduling method and device based on multi-objective ant colony algorithm, storage medium and terminal
  • Vehicle logistics scheduling method and device based on multi-objective ant colony algorithm, storage medium and terminal

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Embodiment Construction

[0038] Those skilled in the art understand that, as mentioned in the background technology, the traditional vehicle logistics scheduling mode does not fully consider the specific scheduling scenarios, does not optimize the loading of the task target, and does not fully consider the constraint requirements of the input order itself. The scheduling plan (that is, the scheduling scheme) is formed simply by manually assigning orders to vehicles. Due to the shortcomings of manual scheduling in the existing vehicle logistics scheduling scheme, there are many shortcomings such as few variables to consider, non-optimal scheduling scheme, low utilization rate of transportation resources, and full order response speed, which cannot be used in practical applications. Satisfying the constraints proposed from the perspective of business contracts and other aspects will cause damage to stakeholders in all aspects of the task, and will result in invalid solutions due to ignorance of some real...

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Abstract

A vehicle logistics scheduling method and device based on a multi-objective ant colony algorithm, a storage medium and a terminal are provided. The method comprises: acquiring vehicle logistics data,the vehicle logistics data comprising order data and capacity data; acquiring M candidate allocation schemes based on the vehicle logistics data, wherein M is more than or equal to 1; marking the candidate allocation schemes as ants, marking the set of M ants as an ant colony, discarding the ants having all objective vectors dominated in the ant colony during transfer of each ant to obtain a non-inferior solution set, and marking the projection of the ant on each objective as an objective vector corresponding to the objective; and when the transfer state of the ant colony satisfies a preset termination condition, selecting an optimal scheduling scheme from the obtained non-inferior solution set according to the service scenario. The solution can realize automatic dispatching of the vehiclelogistics, is beneficial to realizing optimal scheduling, and reduces the dynamic scheduling cost of a freight train on the whole.

Description

technical field [0001] The invention relates to the technical field of automobile logistics, in particular to a vehicle logistics scheduling method and device, a storage medium, and a terminal based on a multi-objective ant colony algorithm. Background technique [0002] Complete vehicle logistics refers to a series of activities and processes in which complete vehicles are transported from OEMs, distribution sites, and dealers to end customers. Vehicle logistics scheduling needs to solve a series of problems such as logistics route planning, loading and vehicle scheduling. [0003] The factors involved in the existing vehicle logistics scheduling are relatively complex, with many constraints and multiple and mutually restrictive objectives, such as OEMs and their warehouses, logistics companies and their transit warehouses, carriers and their contract drivers, dealers and their warehouses, etc. In many aspects, it is a multi-objective optimization problem in general. [00...

Claims

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Application Information

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IPC IPC(8): G06Q10/08G06Q50/28G06N3/00
CPCG06N3/006G06Q10/083G06Q10/08
Inventor 金忠孝梁亮
Owner ANJI AUTOMOTIVE LOGISTICS
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