Research on intelligent scheduling in and out of storage of measuring instruments

This article studies the integrated scheduling problem and optimization method of order batching in the warehouse logistics environment. For the integrated scheduling problem of order batching and job allocation with dynamic order arrival, a perturbed self-learning iterative local search algorithm is studied. To accelerate the search speed of local optimal solutions in the algorithm, a rolling time window batch algorithm is designed that integrates order features; To address the problem of difficulty in tuning disturbance parameters during online solving, a reinforcement learning mechanism integrating multiple evaluation criteria is adopted to design a disturbance parameter self-learning mechanism, which adaptively adjusts the disturbance type and intensity to balance global and local search; To balance algorithm performance and efficiency, this mechanism is applied through offline training and online updates to reduce the computational complexity of online optimization.