This study attempts to solve vehicle routing problem with time window (VRPTW). The study first identifies the real problems and suggests some recommendations on the issues. The technique used in this study is Genetic ...This study attempts to solve vehicle routing problem with time window (VRPTW). The study first identifies the real problems and suggests some recommendations on the issues. The technique used in this study is Genetic Algorithm (GA) and initialization applied is random population method. The objective of the study is to assign a number of vehicles to routes that connect customers and depot such that the overall distance travelled is minimized and the delivery operations are completed within the time windows requested by the customers. The analysis reveals that the problems experienced in vehicle routing with time window can be solved by GA and retrieved for optimal solutions. After a thorough study on VRPTW, it is highly recommended that a company should implement the optimal routes derived from the study to increase the efficiency and accuracy of delivery with time insertion.展开更多
针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行...针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行性规则和改进的epsilon约束处理方法进行更新。在第一阶段,主种群和副种群分别探索约束Pareto前沿(CPF)与无约束Pareto前沿(UPF),从而获取UPF和CPF的位置信息;在第二阶段,设计一种分类方法,根据UPF与CPF的位置对CMOP进行分类,从而对不同类型的CMOP执行特定的进化策略;此外,提出一种随机扰动策略,在副种群进化到CPF附近时,对它进行随机扰动以产生一些位于CPF上的个体,从而促进主种群在CPF上的收敛与分布。把所提算法与6个具有代表性的算法:CMOES(Constrained Multi-objective Optimization based on Even Search)、dp-ACS(dual-population evolutionary algorithm based on Adaptive Constraint Strength)、c-DPEA(DualPopulation based Evolutionary Algorithm for constrained multi-objective optimization)、CAEAD(Constrained Evolutionary Algorithm based on Alternative Evolution and Degeneration)、BiCo(evolutionary algorithm with Bidirectional Coevolution)和DDCMOEA(Dual-stage Dual-population Evolutionary Algorithm for Constrained Multiobjective Optimization)在LIRCMOP和DASCMOP两个测试集上进行实验比较。实验结果表明,DPDSEA在23个问题中取得了15个最优反转世代距离(IGD)值和12个最优超体积(HV)值,展现了DPDSEA在处理复杂CMOP时显著的性能优势。展开更多
叶面积指数(Leaf Area Index, LAI)是反映作物生长的重要参数,准确获取对农业监测和产量评估至关重要。Sentinel-2卫星具备多红边与短波红外波段,在LAI反演中具有潜在优势,因此比较不同模型与波段组合的反演能力,对提升玉米LAI估算精度...叶面积指数(Leaf Area Index, LAI)是反映作物生长的重要参数,准确获取对农业监测和产量评估至关重要。Sentinel-2卫星具备多红边与短波红外波段,在LAI反演中具有潜在优势,因此比较不同模型与波段组合的反演能力,对提升玉米LAI估算精度具有重要意义。本文以位于甘肃省张掖市的黑河遥感试验研究站大满观测场为研究区,基于PROSAIL模型敏感性分析,筛选对LAI敏感的波段组合和关键参数,构建模拟数据库,并采用查找表法(Look Up Table, LUT)、遗传算法(Genetic Algorithm, GA)和随机森林(Random Forest, RF)回归模型3种方法反演LAI,结合Sentinel-2影像及实测数据进行精度验证。结果表明:(1)异常数据对反演精度影响显著,LUT对异常值最敏感(ΔR^(2)=0.20~0.26),RF相对稳定(ΔR^(2)=0.14~0.20),LUT加入红边波段RE_(2)(B,R,RE_(2),RE_(3),NIR,RE,SW_(2))可在提高精度的同时保持抗干扰性(均值ΔR^(2)=0.18);(2)移除异常数据后,LUT反演精度最高(R^(2)=0.88,RMSE=0.31),GA次之,RF加入RE_(2)波段表现显著提升(R^(2)=0.65~0.79,RMSE=0.64~0.53);(3) 3种模型在高LAI区间(2.5~5.0;LUT:R^(2)>0.84;GA:R^(2)>0.71;RF:R^(2)>0.57)反演精度均显著优于低LAI区间(0.5~2.5),其中RF结合RE_(2)波段R^(2)从0.57提高至0.82。综上所述,物理模型反演方法以及RE_(2)波段在提升玉米LAI反演精度方面作用突出,能够为玉米的LAI反演工作和生长监测提供有益参考。展开更多
文摘This study attempts to solve vehicle routing problem with time window (VRPTW). The study first identifies the real problems and suggests some recommendations on the issues. The technique used in this study is Genetic Algorithm (GA) and initialization applied is random population method. The objective of the study is to assign a number of vehicles to routes that connect customers and depot such that the overall distance travelled is minimized and the delivery operations are completed within the time windows requested by the customers. The analysis reveals that the problems experienced in vehicle routing with time window can be solved by GA and retrieved for optimal solutions. After a thorough study on VRPTW, it is highly recommended that a company should implement the optimal routes derived from the study to increase the efficiency and accuracy of delivery with time insertion.
文摘针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行性规则和改进的epsilon约束处理方法进行更新。在第一阶段,主种群和副种群分别探索约束Pareto前沿(CPF)与无约束Pareto前沿(UPF),从而获取UPF和CPF的位置信息;在第二阶段,设计一种分类方法,根据UPF与CPF的位置对CMOP进行分类,从而对不同类型的CMOP执行特定的进化策略;此外,提出一种随机扰动策略,在副种群进化到CPF附近时,对它进行随机扰动以产生一些位于CPF上的个体,从而促进主种群在CPF上的收敛与分布。把所提算法与6个具有代表性的算法:CMOES(Constrained Multi-objective Optimization based on Even Search)、dp-ACS(dual-population evolutionary algorithm based on Adaptive Constraint Strength)、c-DPEA(DualPopulation based Evolutionary Algorithm for constrained multi-objective optimization)、CAEAD(Constrained Evolutionary Algorithm based on Alternative Evolution and Degeneration)、BiCo(evolutionary algorithm with Bidirectional Coevolution)和DDCMOEA(Dual-stage Dual-population Evolutionary Algorithm for Constrained Multiobjective Optimization)在LIRCMOP和DASCMOP两个测试集上进行实验比较。实验结果表明,DPDSEA在23个问题中取得了15个最优反转世代距离(IGD)值和12个最优超体积(HV)值,展现了DPDSEA在处理复杂CMOP时显著的性能优势。
