Industrial tobacco logistics scheduling method based on Lagrange relaxation

Abstract

The invention discloses an industrial tobacco logistics scheduling method based on Lagrange relaxation. The industrial tobacco logistics scheduling method comprises the following steps: 1, obtaining order data; 2, enumerating a feasible path set; 3, establishing a set segmentation model; 4, relaxing the model; 5, initializing parameters; 6, generating an initial feasible solution and updating an upper bound and an optimal solution; 7, updating the current cost of the path; 8, updating the lower bound; 9, judging whether an upper bound updating condition is met or not, if so, entering the step10, and otherwise, entering the step 12; 10, updating the current feasible solution; 11, updating the optimal solution and the Lagrange multiplier; and 12, judging whether a termination condition of algorithm execution is satisfied or not, if so, outputting an optimal solution of the algorithm, and otherwise, returning to the step 7. According to the invention, a good transportation scheduling scheme can be quickly found to reduce the freight expenditure of an industrial tobacco logistics company and improve the service level of the industrial tobacco logistics company, the transportation costof a third-party logistics transportation company executing the scheduling scheme can be reduced, and a win-win situation is achieved.

Description

technical field [0001] The invention belongs to the field of logistics transportation scheduling, in particular to a Lagrangian relaxation-based industrial tobacco logistics scheduling method. Background technique [0002] At present, the development level of logistics-related technologies of industrial companies has begun to directly affect the company's core competitiveness. Nowadays, some industrial tobacco logistics enterprises are at a low level of development. Cigarette transportation scheduling mainly relies on dispatchers to manually combine and load orders based on experience. This most primitive method is extremely inefficient for the current order volume. Only a very small part of the order loading plan can be considered based on experience within limited working hours. This relatively backward scheduling method directly leads to the status quo of low service levels, low transportation efficiency and high transportation costs of industrial tobacco logistics enterp...

AI Technical Summary

Problems solved by technology

Nowadays, some industrial tobacco logistics enterprises are at a low level of development. Cigarette transportation scheduling mainly relies on dispatchers to manually combine and load orders based on experience. This most primitive method is extremely inefficient for the current order volume. Only a very small part of the order loading plan can be considered based on experience within limited working hours. This relatively backward scheduling method directly leads to the status quo of low service levels, low transportation efficiency and high transportation costs of industrial tobacco logistics enterprises.

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