A Graph Theory-based Approach to the Optimization of AGV Quantity Configuration and Scheduling
To improve the operational efficiency of automated guided vehicle(AGV)systems in unmanned warehouses,the problem of AGV quantity configuration and scheduling in large-scale scenarios was investigated.With the objective of minimizing the number of AGVs and the total transportation cost of AGVs,a shared network of AGVs was constructed by abstracting the spatio-temporal constraints between tasks,and transformed the quantity configuration and scheduling problem into a weighted minimum path coverage problem in graph theory.The calculation results show that compared with the traditional mathematical planning model,the graph theory method is efficient and stable in solving large-scale scenarios.It can complete tasks with fewer AGVs and minimal corresponding transportation costs while meeting task time requirements.For the quantity configuration and scheduling problem of 300 tasks,the graph theory method can solve it in 4 seconds,which is three orders of magnitude faster than the mathematical programming model,and the number of AGVs decreased by 10.3％.
warehouse logisticsAGV quantity configuration and schedulingspatio-temporal networkweighted minimum path coverage
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