Weight learning based stack shape planning method

Abstract

The invention discloses a weight learning based stack shape planning method. The concrete steps include: step 1, calculating stacking priority [Ri] for all to-be-stacked cargo tanks, wherein Ri=PvVi+PsSi+PwWi; step 2, ranking Ri in sequence from large to small; step 3, taking the current [Ri]max cargo container and trying to stack it on L1 to Lk in sequence until a stackable stack shape layer Lm is found; step 4, update the current stack shape state; step 5, removing the cargo containers counted to the stack shape from Ri; step 6, checking whether residual cargo containers exist or not, if residual cargo containers exist, repeating steps from step 3 to step 5; step 6, implementing stack shape planning. The method is simple in use and parameters are clear and easy to understand. Automatic stack shape planning can be implemented in a comparatively short period and dynamic planning can even be made for each stack order; high efficiency is achieved. The method provided by the invention isfully automatic and no manual intervention is required. The method provided by the invention has a self learning function and the quality of planned stack shapes is improved continuously towards the optimum.

Description

technical field [0001] The invention relates to the field of logistics, in particular to a stack shape planning method based on weight learning. Background technique [0002] The application of automation equipment in the logistics industry, such as express packing, palletizing, etc., has replaced the role of handling by humans, and new application requirements have also put forward new requirements for the degree of intelligence of automation equipment. The traditional manual palletizing process is divided into two types: the first one, manual palletizing: through human participation, the position where the container should be stacked is continuously and dynamically determined. The disadvantages are low efficiency, long palletizing cycle, and lack of macroscopic Planning, when there are many boxes to be stacked, the dynamic artificial stack shape planning may fail, and it needs repeated trials, which greatly depends on the experience of the workers to get a better stack sha...

AI Technical Summary

Problems solved by technology

The traditional manual palletizing process is divided into two types: the first one, manual palletizing: through human participation, the position where the container should be stacked is continuously and dynamically determined. The disadvantages are low efficiency, long palletizing cycle, and lack of macroscopic Planning, when there are many boxes to be stacked, the dynamic artificial stack shape planning may fail, and it needs repeated trials, which greatly depends on the experience of the workers to get a better stack shape; the second type, semi-automatic palletizing: automatic The equipment is responsible for performing the stacking process, while the manual is responsible for the planning of the stacking shape. Through the previously known size parameters and load-bearing capacity of the container, manually use some stacking shape planning software to determine the stacking shape

The disadvantage is that the degree of automation is low. Once the size and type of the container change, manual re-planning is required, and the efficiency is low.

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