Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.

Heterodyne detection enhanced by quantum correlation

Heterodyne detectors as phase-insensitive(PI) devices have found important applications in precision measurements such as space-based gravitational-wave(GW) observation.However, the output signal of a PI heterodyne detector is supposed to suffer from signal-to-noise ratio(SNR) degradation due to image band vacuum and imperfect quantum efficiency.Here, we show that the SNR degradation can be overcome when the image band vacuum is quantum correlated with the input signal.We calculate the noise figure of the detector and prove the feasibility of heterodyne detection with enhanced noise performance through quantum correlation.This work should be of great interest to ongoing space-borne GW signal searching experiments.