带时间窗的地铁配送网络路径优化问题

为应对人们日益增加的货物需求与货车进城难题,提出整合地铁网和道路交通网,形成以地铁列车和城市配送车辆为载体的地铁配送网络.考虑列车开行时间表、客户服务时间窗、城市配送车辆容量等限制条件,构建带时间窗的地铁配送网络路径优化模型,综合优化地铁列车班次的客户分配、出站点的客户分配及末端配送路径.设计随机变邻域的迭代搜索算法(ILS-RVND)进行求解,以成都市地铁3号线运输货物为例,验证了模型和算法的实用性和有效性.结果表明,地铁配送网络配送成本低,准时性高,配送车辆行驶距离短,能满足比货车单独配送更精准的服务需求.     

Comparison of vibration response prediction on the main deck of a catamaran using experimental,numerical and correlated mode shapes

This work analyses the influence of three types of modal matrices on the prediction of vibration response (virtual sensing), at unmeasured degree of freedoms (DOFs), on a catamaran’s main deck: (1) uncorrelated finite element (FE), (2) correlated FE and (3) experimental modal matrices.A multi-objective genetic algorithm (MOGA) framework was developed to handle the optimization and prediction processes. This framework introduces a new metric called Time–Frequency-Error Response Assurance Criteria (TFERAC) to assess the prediction quality. This metric also allows estimating the best set of modal acceleration vectors, which is a critical step in the virtual sensing process.As a case study, only 06 accelerometers and 13 vibration modes (within each modal matrix) were used in the virtual sensing. The MOGA framework’s performance was evaluated using a variance analysis test (ANOVA) between measured and predicted response signals.Results showed that: (1) any one of the modal matrices could be used successfully for virtual sensing on the main deck, that is, there is no need to use correlated FE or experimental modal matrices; (2) the newly proposed metric TFERAC leads to smaller errors in the prediction of both vibration time series and vibration spectra;(3) it is possible to perform a virtual sensing on a ship’s main deck using a limited number of sensors and a numerical modal matrix without being correlated with experimental data.