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Collaborative vehicle path optimization method based on shared carrier and shared warehouse

A technology of vehicle routing and optimization methods, applied in the field of logistics, which can solve the problems of single target, neglect of transportation plans, and neglect of bilateral cooperation on the supply side and demand side.

Active Publication Date: 2019-10-11
ZHEJIANG UNIV OF FINANCE & ECONOMICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above model based on carrier collaboration only considers unilateral supply-side collaboration, while ignoring the bilateral collaboration between supply and demand sides, which may lead to the neglect of potentially more optimal transportation solutions
In addition, most of these collaborative vehicle routing literatures take cost or time as the optimization goal, and the goal is relatively single, which cannot fully reflect logistics factors

Method used

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  • Collaborative vehicle path optimization method based on shared carrier and shared warehouse
  • Collaborative vehicle path optimization method based on shared carrier and shared warehouse
  • Collaborative vehicle path optimization method based on shared carrier and shared warehouse

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0153] As an example with two carriers and four warehouses, Figure 4 (a) A transport scheme solution for this example is given. Figure 4 (b) The transportation scheme solution based on matrix representation is given. Such as Figure 4 As shown in (b), the first row in the carrier coordination matrix shows that the goods delivered by carrier A to warehouse 1 are delivered by carrier A itself, and the goods delivered by carrier A to warehouse 2 and warehouse 4 are delivered by carrier B , warehouse 3 has no cargo demand for carrier A. Likewise, the second row in the carrier coordination matrix represents the shipment by carrier B.

[0154] The first row in the warehouse coordination matrix indicates that warehouse 1 and warehouse 2’s cargo demand for carrier A is delivered to warehouse 2, warehouse 4’s cargo demand for carrier A is delivered to warehouse 3, and warehouse 3 has no cargo for carrier need. Similarly, the second row in the warehouse coordination matrix repres...

Embodiment 2

[0174] Such as Figure 5 As shown, the solution of two carriers and six warehouses is taken as an example. First, split a carrier vector into two vectors of zeros and non-zeros, keeping the relative positions of these elements unchanged. This splitting guarantees that the position of the zeros is fixed (see splitting stages). Then, a reallocation operation is taken on the non-zero vector and a new non-zero vector is produced (see reallocation phase). Finally, the new non-zero and zero vectors are combined into a complete vector according to their relative positions (see combine phase). Perform the reallocation sub-operation on another carrier in a similar manner, and the solution after the reallocation operation is obtained after completion.

[0175] Although the exchange sub-operation and reallocation sub-operation included in the vibration operation can generate new solutions, the solution space is not large enough because the elements that make up each carrier, warehouse...

Embodiment 3

[0212] Select two groups of experimental cases. The first group contains 12 experimental cases, numbered #1001 to #1012. All warehouses in this group of cases are randomly distributed within 100 square meters. The second group also contains 12 experimental cases, numbered from #2001 to #2012, and the warehouses in this group of cases are generated by clustering. The number of clusters in the second group of cases is set to 3 or 5, and within each class, the distance between any two warehouses is less than 15.

[0213] In two sets of cases, the number of warehouses is set to be 10, 15, 20, 25, 30 or 100; the vehicle loading capacity is 100, 200, 1000 or 2000; and the warehouse storage capacity is 200, 300, 2000 or 3000. Each case contains two carriers. Compared with the traditional shared customer collaborative vehicle routing model SCC-VRP model, the CVRP-SCD model of this embodiment adds some new objectives, such as transit time, service quality and service reliability, ther...

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Abstract

The invention discloses a collaborative vehicle path optimization method based on a shared carrier and a shared warehouse. The collaborative vehicle path optimization method comprises the following steps: establishing a collaborative vehicle path optimization model based on the shared carrier and the shared warehouse; initializing to obtain a current solution, and representing the current solutionby adopting block coding; performing vibration operation on the current solution to obtain a neighborhood set containing a plurality of neighborhood solutions; mutation operation is carried out on each neighborhood solution, and a neighborhood set is updated; performing local search operation on the updated neighborhood set to obtain a globally optimal solution; if the function value of the globally optimal solution is greater than the function value of the current solution, taking the globally optimal solution as the current solution; otherwise, keeping the current solution unchanged; judging whether a preset termination condition is met or not, and if the preset termination condition is met, outputting a latest current solution, namely an optimal transportation scheme; otherwise, continuing iteration. Bilateral logistics cooperation of the supply side and the demand side is considered, and a more comprehensive and better transportation scheme is obtained in combination with multi-target analysis.

Description

technical field [0001] The application belongs to the technical field of logistics, and in particular relates to a collaborative vehicle route optimization method based on shared carriers and shared warehouses. Background technique [0002] In recent years, with the increasing awareness of sustainable development, green supply chain has received more and more attention. As an important part of the green supply chain, the transportation industry should strive to realize the sustainable development of logistics and transportation in order to improve transportation efficiency. In general, carriers provide delivery services independently, resulting in high transportation costs, pollution, vehicle space occupation, and high traffic volumes. Although some efficient optimization algorithms and data mining methods have been used to solve this problem, due to the limitations of the model, the logistics efficiency has not been effectively improved. Therefore, researchers are increas...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/08
CPCG06Q10/047G06Q10/08355Y02T10/40
Inventor 张帅蔡怡帅张文宇陈子旋
Owner ZHEJIANG UNIV OF FINANCE & ECONOMICS