Multi-crown-block scheduling optimization method and system
An optimization method and crane technology, applied in the field of information, can solve problems such as the complexity and dynamic randomness of crane scheduling that cannot be truly reflected, and affect the efficiency of cranes.
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Embodiment 1
[0083] refer to figure 1 , the present invention provides a multi-crane vehicle scheduling optimization method, comprising:
[0084] Step S1: Match the tasks and cranes of the day in the steelmaking-refining-continuous casting workshop, and obtain n task-crane sets, where n represents the number of matching tasks-cradle sets;
[0085] Step S2: Establish a multi-crane train dynamic scheduling model, and obtain the travel time of all cranes when all tasks in each task-crane train set are completed;
[0086] Step S3: Update and optimize the n task-crane sets by using double tangent point crossover genetic algorithm based on real number coding, and take the shortest travel time of all cranes when all tasks are completed as the objective function, and obtain the travel time of all cranes when all tasks are completed The shortest sky-car driving path.
[0087] It should be noted that the present invention does not limit the sequence of step S1, step S2 and step S3.
[0088] In th...
Embodiment 2
[0146] Such as Figure 10 As shown, the present invention also provides a multi-crane vehicle scheduling optimization system, including:
[0147] The matching module M1 is used to match the tasks and cranes of the steelmaking-refining-continuous casting workshop on the day, and obtain n task-crane sets, where n represents the matching number of task-cradle sets;
[0148] The time calculation module M2 is used to establish a multi-crane train dynamic scheduling model, and obtain the travel time of all cranes when all tasks in the set of each task-crane train are completed;
[0149] The shortest path calculation module M3 is used to update and optimize the n task-crane sets by means of a double tangency crossover genetic algorithm based on real number coding, and take the shortest travel time of all cranes when all tasks are completed as the objective function to obtain the When all tasks mentioned above are completed, the traveling time of all cranes is the shortest traveling ...
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