在炼钢-连铸生产过程中,钢包的优化调度对减少钢铁生产的热能消耗,提高生产效率具有重要作用.将钢包调度问题归结为任务带有时间窗、车辆带有调整时间的车辆路径问题(vehicle routing problem with time windowsand adjustment time,VRP...在炼钢-连铸生产过程中,钢包的优化调度对减少钢铁生产的热能消耗,提高生产效率具有重要作用.将钢包调度问题归结为任务带有时间窗、车辆带有调整时间的车辆路径问题(vehicle routing problem with time windowsand adjustment time,VRPTW-AT).根据钢包服务钢水过程的约束建立了钢包调度问题的数学模型,针对模型特点提出了分散搜索(scatter search,SS)算法.基于国内某钢铁企业生产实绩做测试案例,对SS算法的优化效果与钢厂生产的实际数据进行了对比分析,实验结果表明了模型和算法的有效性.展开更多
带时间窗车辆调度问题(Vehicle Routing Problem with Time Window,VRPTW)是具有时间区间(即时间窗)约束的车辆调度问题,它比传统的车辆调度问题更加接近实际中的运输要求。本文从分析模拟退火算法的求解思想入手,建立一个利用模拟退火...带时间窗车辆调度问题(Vehicle Routing Problem with Time Window,VRPTW)是具有时间区间(即时间窗)约束的车辆调度问题,它比传统的车辆调度问题更加接近实际中的运输要求。本文从分析模拟退火算法的求解思想入手,建立一个利用模拟退火算法求解VRPTW问题的数学模型,并结合南宁铁路局南宁机务段多个检修基地物料配送的实际,求解出配送车辆最优派车方案,为企业节支创效提供技术支持。展开更多
The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic ...The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.展开更多
针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and im...针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and improved large neighborhood search algorithm,K-means-ILNSA)。采用先聚类后优化的策略,利用K-means算法对待配送客户进行分组,以提高优化效率。采用遗传算法对聚类产生的每组客户进行单独优化,以初步规划配送路径。引入大规模邻域搜索(large neighborhood search,LNS)算法对配送路径进一步优化,以有效避免算法陷入局部最优解。实验结果表明:所提算法能够有效解决带时间窗的车辆路径问题,其生成的车辆总路程短,优化求解效率高。展开更多
文摘在炼钢-连铸生产过程中,钢包的优化调度对减少钢铁生产的热能消耗,提高生产效率具有重要作用.将钢包调度问题归结为任务带有时间窗、车辆带有调整时间的车辆路径问题(vehicle routing problem with time windowsand adjustment time,VRPTW-AT).根据钢包服务钢水过程的约束建立了钢包调度问题的数学模型,针对模型特点提出了分散搜索(scatter search,SS)算法.基于国内某钢铁企业生产实绩做测试案例,对SS算法的优化效果与钢厂生产的实际数据进行了对比分析,实验结果表明了模型和算法的有效性.
文摘带时间窗车辆调度问题(Vehicle Routing Problem with Time Window,VRPTW)是具有时间区间(即时间窗)约束的车辆调度问题,它比传统的车辆调度问题更加接近实际中的运输要求。本文从分析模拟退火算法的求解思想入手,建立一个利用模拟退火算法求解VRPTW问题的数学模型,并结合南宁铁路局南宁机务段多个检修基地物料配送的实际,求解出配送车辆最优派车方案,为企业节支创效提供技术支持。
文摘The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.
文摘针对现有优化算法在求解带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)时存在易陷入局部最优解和收敛速度慢等问题,提出了一种基于K均值聚类和改进大规模邻域搜索算法(K-means clustering algorithm and improved large neighborhood search algorithm,K-means-ILNSA)。采用先聚类后优化的策略,利用K-means算法对待配送客户进行分组,以提高优化效率。采用遗传算法对聚类产生的每组客户进行单独优化,以初步规划配送路径。引入大规模邻域搜索(large neighborhood search,LNS)算法对配送路径进一步优化,以有效避免算法陷入局部最优解。实验结果表明:所提算法能够有效解决带时间窗的车辆路径问题,其生成的车辆总路程短,优化求解效率高。
