Cigarette delivery multi-warehouse-point multi-direction combined transport scheduling double-layer optimization algorithm
A double-layer optimization, multi-directional technology, applied in the field of cigarette logistics, can solve the problems of infeasible solution of network convergence, poor parameter robustness, etc., and achieve the effect of multi-storage multi-directional dynamic scheduling.
Active Publication Date: 2021-05-25
HONGYUN HONGHE TOBACCO (GRP) CO LTD
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[0005] (2) The network may converge to an infeasible solution to the problem;
[0006] (3) The final result of network optimization depends largely on the parameters of the network, that is, the parameters are less robust
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[0044]The technical solutions of the present invention are further described in connection with the accompanying drawings and specific embodiments.
[0045]At the same time, due to the traditional Hopfield network still use a gradient drop policy, the vehicle path optimization calculation based on Hopfield network is usually caused by the following issues:
[0046](1) The network ultimately converges to local extremely low, not the global optimal solution;
[0047](2) The network may converge into the problem of problem;
[0048](3) The final result of network optimization is largely dependent on the network parameters, that is, the parameter robustness is poor.
[0049]In order to solve the above disadvantages of traditional HOPField neural networks, and make algorithms more suitable for solving the homogeneous scheduling problem of tobacco stream, the HopField neural network is proposed to combine the combination of analog annealing algorithm and Levy flight strategy. The specific binding method...
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The invention relates to a cigarette delivery multi-warehouse point multi-direction combined transport scheduling double-layer optimization algorithm, which belongs to the field of cigarette logistics, and introduces an improved Hopfield neural network algorithm (IHNN) of a simulated annealing algorithm and a Levy flight strategy as a global optimization algorithm. The cigarette finished product delivery combined transport scheduling method based on the improved Hopfield neural network algorithm is formed. Meanwhile, an order pool combination dynamic planning algorithm is combined to carry out order pool stowage optimization and an optimal vehicle selection planning algorithm is combined to select a goods allocation vehicle, so that multi-warehouse-point multi-direction dynamic scheduling is realized. According to the method, the problem of multi-warehouse-point and multi-direction vehicle scheduling is a multi-target complex vehicle path problem of dynamic order arrival, and is also a core problem faced by finished product logistics warehousing operation scheduling optimization of tobacco industry enterprises.
Description
Technical field[0001]The present invention relates to the field of cigarette logistics, and more particularly to a multi-directional intermodal scheduling double layer optimization algorithm for cigarette delivery multifuadaBackground technique[0002]Particle group algorithm, whale optimization algorithm has been widely used in the field of vehicle scheduling, but also has a good effect, but there are some problems, and there is a problem that the whale optimization algorithm is difficult to coordinate, and it is easy to develop in a local superiority. insufficient. For tobacco logistics scheduling issues, it is solved large, small feasible domain, and traditional whale optimization algorithm showed weak search.[0003]At the same time, due to the traditional HopField network still use a gradient drop policy, the HopField network-based vehicle path optimization calculation usually results in the following issues:[0004](1) The network ultimately converges to local extremely low, not the...
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IPC IPC(8): G06Q10/04G06N3/00G06N3/04G06N3/08G06Q10/06G06Q10/08
CPCG06Q10/04G06Q10/06315G06Q10/087G06N3/08G06N3/006G06N3/045Y02T10/40
Inventor 安裕强徐跃明欧阳世波陈晓伟王磊迟文超谢俊明李柏宇余丽莎王康王鹍秦希
Owner HONGYUN HONGHE TOBACCO (GRP) CO LTD