Abstract
To improve the delivery efficiency of automated storage and retrieval system, the problem of the integrated optimization of mixed cargo packing and cargo location assignment is addressed. An integrated optimization model of mixed cargo packing and location assignments with the shortest time for the stacker in a certain historical period is established and is transformed into a conditional packing problem. An improved hybrid genetic algorithm based on a group coding method is designed to solve the problem. When the initial population is generated, a new heuristic algorithm is designed to improve the convergence speed of the genetic algorithm considering the correlation and frequency of the goods outbound. A heuristic algorithm for a two-dimensional rectangular-packing problem is designed to determine whether a variety of goods can be mixed in packing. Taking actual data from an automated storage and retrieval system for an aviation food company as an example, the established model and design algorithm are verified and the influence of changes in the outbound delivery orders on the optimization result is analyzed. The results show that compared to the method of separate storage of goods based on cube-per-order index rules and a phased optimization method of mixed storage of goods, an integrated optimization method of mixed cargo packing and location assignment can improve the outbound delivery efficiency of the stacking machine by 11.43–25.98% and 1.73–5.51%, respectively, and reduce the cargo location used by 50–55% and 0–10%, respectively. The stronger the correlation of the goods leaving a warehouse, the greater the potential of the design method in this paper to improve the efficiency of the stacker.
Highlights
In recent years, with the rapid development of e-commerce, intelligent manufacturing, air transportation, and other fields, higher requirements have been proposed for the input–output efficiencies of warehouses and logistics
The results show that compared to the method of separate storage of goods based on cube-per-order index rules and a phased optimization method of mixed storage of goods, an integrated optimization method of mixed cargo packing and location assignment can improve the outbound delivery efficiency of the stacking machine by 11.43–25.98% and 1.73–5.51%, respectively, and reduce the cargo location used by 50–55% and 0–10%, respectively
This paper focuses on the problem of the integrated optimization of mixed cargo packing and cargo location assignment; a hybrid genetic algorithm based on population expression coding and an embedded heuristic algorithm is designed to solve the optimization model established in the previous section
Summary
With the rapid development of e-commerce, intelligent manufacturing, air transportation, and other fields, higher requirements have been proposed for the input–output efficiencies of warehouses and logistics. When a container stores only one type of goods (known as “separate cargo packing”), the cargo packing is optimized mainly to increase the loading rate of the container and reduce the number of used containers, without much impact on the outbound efficiency of AS/RS. An outbound delivery order often contains a variety of goods. If goods that are often in the same outbound delivery order are mixed and stored in the same container, it can greatly improve the outbound efficiency of the AS/RS. The probability that two different items are required by the same order is defined as the outbound correlation. Mixed packing and cargo location assignment are strongly correlated, which has a great impact on the efficiency of AS/RS. The integrated optimization of mixed cargo packing and cargo location assignment must be studied
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