Abstract
To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV- picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.
Highlights
In warehouse operations, picking operations account for an increasing proportion of costs, accounting for about 55% of warehouse operation costs [1]
Variables are defined: q, q for retrieval picking task index sequence, q, q = 1, 2, · · ·, Q, Q is the number of retrieval picking tasks, and q, q are continuous tasks before and after; k is the index sequence of automated guided vehicles (AGV), k = 1, 2, · · ·, K, K is the number of AGVs, and K should be take integer values; h is the index sequence of the picking stations, h = 1, 2, · · ·, H, H is the number of picking stations; tqh1 is the time from the initial position of the rack corresponding to
A two-stage mathematical model of the AGV unmanned warehouse was established to improve the throughput of AGV unmanned warehouse and the efficiency of outbound operation
Summary
In warehouse operations, picking operations account for an increasing proportion of costs, accounting for about 55% of warehouse operation costs [1]. H. Tang et al.: Research on Equipment Configuration Optimization of AGV Unmanned Warehouse and the picking stations, that is, reduce the task execution time. Based on the research background of an unmanned warehouse in a factory, this paper optimizes the configuration of AGVs and picking stations equipment, the combination of AGVs and picking stations collaborative scheduling. We set up the AGV unmanned warehouse task scheduling, equipment configurations and the layout of the two-stage model. The task allocation and task execution order of AGVs, the quantity configuration of AGVs and the layout location, allocation and quantity of picking stations will all affect the operation efficiency and operation cost of unmanned warehouse. Reasonable task scheduling can improve the picking efficiency of unmanned warehouses, and reduce the number of AGVs and picking stations. The equipment configuration combination problem can be turned into a multiple backpack problem [27], [28]
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