Meta-heuristic-algorithm-based dynamic marshalling scheduling optimization method

A meta-heuristic algorithm and optimization method technology, applied in the direction of calculation, genetic model, genetic law, etc., can solve the problems of poor practicability, achieve good practicability, and improve the efficiency of marshalling and scheduling

Inactive Publication Date: 2017-09-29
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0007] In order to overcome the shortcomings of poor practicability of the existing dynamic marshalling scheduling optimizatio

Method used

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  • Meta-heuristic-algorithm-based dynamic marshalling scheduling optimization method
  • Meta-heuristic-algorithm-based dynamic marshalling scheduling optimization method
  • Meta-heuristic-algorithm-based dynamic marshalling scheduling optimization method

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Embodiment Construction

[0026] refer to Figure 1-3 . The present invention is based on meta-heuristic algorithm The specific steps of the dynamic group scheduling optimization method are as follows:

[0027] To complete the entire shunting operation, the sum of the path lengths traveled by the tractor is:

[0028]

[0029] in Indicates the number of parking spaces experienced by the tractor during the i-th shunting, Indicates the initial lane parking space of the tractor in the current shunting operation, Indicates the final parking space of the tractor in the current shunting operation, and also represents the initial lane parking space of the tractor in the next shunting operation. Then, the objective function can be expressed as:

[0030]

[0031] (a) Taking 7 lanes as an example, there are 10 parking spaces in one lane, so the parking spaces in one lane can be expressed as a set DW 1 ={P 1,1 ,P 1,2 ,P 1,3 ......P 1,10}, and so on, the parking spaces from two lanes to seven lane...

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Abstract

The invention discloses a meta-heuristic-algorithm-based dynamic marshalling scheduling optimization method, thereby solving a technical problem of poor practicality of the existing dynamic marshalling scheduling optimization method. The method comprises: according to a shunting instruction designated by a user, a stage shunting plan is established; a solution space is determined based on a lane and parking space and a fitness function of a genetic algorithm is designed based on the sum of lengths of paths for completing all scheduling work by a motor tractor; with the global search ability of the meta-heuristic algorithm, optimal solutions or approximate optimal solutions of a tractor target lane and parking space in each work are found out; and then on the basis of the stage shunting plan, a scheduling work plan is provided. The test demonstrates that the method is suitable for marshalling scheduling optimization of all marshalling stations to improve the marshalling scheduling efficiency of the marshalling stations; and the practicability is high.

Description

technical field [0001] The invention relates to a method for optimizing dynamic grouping scheduling, in particular to a method for optimizing dynamic grouping scheduling based on a meta-heuristic algorithm. Background technique [0002] The freight train marshalling plan is one of the guiding documents for the marshalling station to complete the freight train dispatching operation. A scientific and reasonable marshalling plan is the premise and guarantee for the efficient work of the marshalling station. In recent years, domestic and foreign studies on the marshalling scheduling optimization of marshalling stations have established a certain foundation, but because it is a super-large-scale combinatorial optimization problem, the actual unmarshalling scheduling operation is still mainly based on manual calculations. The typical manual calculation method is the exhaustive method, which calculates all feasible marshalling scheduling schemes according to the time-effective calc...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G06N3/12
CPCG06N3/126G06Q10/047G06Q50/30
Inventor 郑炜刘文兴蔺军胡圣佑
Owner NORTHWESTERN POLYTECHNICAL UNIV
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