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
针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRP...针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRPMC),通过订单拆分处理异构需求,构建以运输成本最小化为目标的混合整数规划模型,并设计增强型自适应大邻域搜索(enhanced adaptive large neighborhood search,EALNS)算法进行求解。该算法融合K-means聚类、节约算法和贪婪重组策略生成初始解,采用自适应大邻域搜索算法避免早熟收敛,结合2-opt邻域操作与模拟退火Metropolis准则实现深度优化。最后,采用Gurobi求解器与自适应大邻域搜索(ALNS)、遗传算法(genetic algorithm,GA)和蚁群算法(ant colony optimization,ACO)进行标准案例测试,验证模型正确性与算法性能。结果表明:EALNS在保证解质量的前提下,求解效率显著提升(求解时间仅为Gurobi的2%);相较于对比算法,其求解质量提升13%~35%,解稳定性提高20%~40%,展现出更优的收敛性能和鲁棒性。研究成果为复杂物流环境下多车场的协同配送提供了高效解决方案,有效拓展了车辆路径优化理论在实际物流场景中的应用范围。展开更多
The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a nove...The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.展开更多
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.
文摘针对现有多车场车辆路径问题研究多局限于同质商品配送的现状,提出考虑车场商品库存差异与客户多商品需求的多车场协同配送车辆路径问题(multi-depot collaborative distribution vehicle routing problem with multi-commodity,MDCDVRPMC),通过订单拆分处理异构需求,构建以运输成本最小化为目标的混合整数规划模型,并设计增强型自适应大邻域搜索(enhanced adaptive large neighborhood search,EALNS)算法进行求解。该算法融合K-means聚类、节约算法和贪婪重组策略生成初始解,采用自适应大邻域搜索算法避免早熟收敛,结合2-opt邻域操作与模拟退火Metropolis准则实现深度优化。最后,采用Gurobi求解器与自适应大邻域搜索(ALNS)、遗传算法(genetic algorithm,GA)和蚁群算法(ant colony optimization,ACO)进行标准案例测试,验证模型正确性与算法性能。结果表明:EALNS在保证解质量的前提下,求解效率显著提升(求解时间仅为Gurobi的2%);相较于对比算法,其求解质量提升13%~35%,解稳定性提高20%~40%,展现出更优的收敛性能和鲁棒性。研究成果为复杂物流环境下多车场的协同配送提供了高效解决方案,有效拓展了车辆路径优化理论在实际物流场景中的应用范围。
基金Fundamental Research Funds for the Central Universities(2024JBZX038)National Natural Science F oundation of China(62076023)。
文摘The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.
