Ship piloting and scheduling method based on forest model and improved genetic algorithm

Abstract

The invention discloses a ship piloting and scheduling method based on a forest model and an improved genetic algorithm. According to the method, the piloting task forest model is established according to piloting task linkage conditions, and the improved breadth-first search algorithm is adopted for traversing a piloting task forest to obtain an initial piloting task set; according to the initial piloting task set, the improved genetic algorithm is adopted for obtaining the optimal piloting and scheduling plan. Various written scheduling rules and unwritten scheduling habits adopted during piloting and scheduling are considered, meanwhile, constraint conditions involved in piloting and scheduling problems are converted into a cost model, and the ship piloting and scheduling method is worked out from the three aspects of feasibility, fairness and benefits.

Description

technical field [0001] The invention belongs to a pilotage task scheduling method in pilotage dispatching, in particular to a pilotage scheduling problem model of a forest model and a solution method based on an improved genetic algorithm. Background technique [0002] my country is a big port country. However, with the rapid development of ports, my country's ports are relatively backward in the efficiency of pilotage dispatching, which seriously restricts the core competitiveness of ports and causes a certain waste of resources. Pilotage companies urgently need to establish an efficient and feasible pilotage scheduling system to optimize the allocation of pilotage resources and improve port competitiveness. The management focus of pilotage scheduling is on pilotage scheduling. Therefore, to explore the modeling optimization and effective solutions of pilotage scheduling in pilotage scheduling has become a key theoretical problem to be solved urgently. [0003] Pilot sche...

AI Technical Summary

Problems solved by technology

The pilotage scheduling model used in the existing methods when constructing the initial pilotage task group makes the intelligent optimization algorithm inevitably increase the work of eliminating infeasible solutions, which increases the difficulty of solving;

[0006] 2. Existing methods only consider finding feasible scheduling results within the scope of pilotage rules, but do not comprehensively consider the waiting time between pilotage tasks, the time balance of pilotage task groups, and the cost issues caused by the transfer of the beginning and end locations of pilotage task groups

[0007] 3. The search ability of the basic intelligent optimization algorithm is limited, especially when the number of pilotage tasks is large and the number of considerations increases, the search for the optimal solution will appear powerless

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