Order batching and picking path joint optimization method

Abstract

The invention discloses an order batching and picking path joint optimization method. Establishing an order batch and picking path joint optimization model taking the shortest total picking time as atarget function; in view of the complexity of dual optimization, a nested genetic algorithm is designed for solving, an outer layer continuously optimizes an order batching result for an order batching problem, and an inner layer optimizes a picking path according to an outer layer order batching result for a path optimization problem. A simulation result shows that compared with a step-by-step optimization strategy, the path distance obtained by the nested genetic algorithm is reduced by 6.9%, and the picking time is reduced by 6%. The method can effectively solve a problem of order batch andpicking path joint optimization, and provides a basis for the optimization of a distribution center picking system.

Description

technical field [0001] The invention relates to order picking path optimization, in particular to a joint optimization method for order batching and picking path based on nested genetic algorithm. Background technique [0002] According to statistics, in the various operations of the picking system in the distribution center, the picking cost accounts for about 60% of the total logistics cost of the distribution center, and is the link that consumes the most time in the distribution center, so it is very important to optimize the picking operation link . Picking operation optimization includes order batching and path optimization, among which order batching is the premise of path optimization and the key to picking operation optimization. [0003] At present, the order batching and picking path mainly adopts step-by-step optimization, that is, the batching results are obtained through different order batching algorithms, and then the path optimization is performed on the ba...

AI Technical Summary

Problems solved by technology

[0004] Algorithms currently proposed to solve optimization problems include genetic algorithm, simulated annealing algorithm, ant colony algorithm, particle swarm algorithm, etc., but these algorithms cannot solve the joint optimization problem of order batching and picking paths

Because in the joint optimization model, order batching and route optimization are related to each other, each batching result that meets the constraints corresponds to a total picking time, and when the batching result changes, the total picking time changes accordingly. It is difficult to obtain the overall optimal solution by step-by-step optimization of batch and picking paths

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