基于改进灰狼优化算法的自动化立体仓库作业能量优化调度

Considering the deadline constraint in the process of loading and unloading goods in automated warehouse, the energy consumption of stacker in scheduling process was set as the optimization objective, and a mathematical model with corresponding penalty function was established. For the inbound tasks, considering both the locating storage and the random storage strategy, a nearest neighbor location selection strategy was adopted to allocate the goods to the stochastic storage. An improved Grey Wolf Optimizer (GWO) was adopted to solve this problem, which had introduced a hybrid solution updating strategy with Lévy flight principle and a multi-population reorganization strategy to enhance search efficiency. The simulation results showed that the improved GWO was effective in solving the energy optimization scheduling problem of automated warehouse.