基于BSO_ALNS算法的城市低碳物流路径优化  

Optimization of Urban Low-carbon Logistics Path Based on BSO_ALNS Algorithm

作　　者：蔡启文 马继东 CAI Qi-wen;MA Ji-dong(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,China)

机构地区：[1]东北林业大学机电工程学院,哈尔滨150040

出　　处：《科学技术与工程》2025年第9期3921-3930,共10页Science Technology and Engineering

基　　金：国家自然科学基金(31870537)。

摘　　要：城市物流终端配送的路径规划是控制运输成本的关键。为解决城市低碳物流的路径规划问题,提出一种头脑风暴-自适应大邻域搜索算法(brain storm optimization_adaptive large neighborhood search,BSO_ALNS)进行求解。首先,建立以最小车辆运输总成本为优化目标的基于车辆油耗的带容量和时间窗约束的低碳车辆路径模型(capacitated vehicle routing problem with time windows,CVRPTW)。其次,利用头脑风暴算法(brain storm optimization,BSO)全局搜索,采用贪婪策略提高初始解质量,引入启发式交叉策略提升全局搜索质量;利用自适应大邻域搜索算法(adaptive large neighborhood search,ALNS)局部搜索,设计10种破坏和修复算子,引入自适应权重机制,结合模拟退火准则避免陷入局部最优。通过选取Solomon中不同规模的C、R、CR等类型实例对BSO_ALNS算法进行性能测试。以最短路径距离为目标,BSO_ALNS算法解与历史最优解误差均在1.5%内;以最小车辆运输总成本为目标,对比BSO和ALNS,BSO_ALNS均取得最优解。证明所提算法能够有效地解决城市低碳物流路径优化问题。The layout of the distribution path of urban logistics terminals is the key to controlling transportation costs.In order to solve the path planning problem of urban low-carbon logistics,a brainstorming-adaptive large neighborhood search algorithm(BSO_ALNS)was proposed.Firstly,a low-carbon vehicle path model with capacity and time window constraints based on vehicle fuel consumption(CVRPTW)was established with the optimization goal of minimum total vehicle transportation cost.Secondly,the brainstorming algorithm(BSO)was used to improve the quality of the initial solution,and the heuristic crossover strategy was introduced to improve the quality of the global search.Using the adaptive large neighborhood search(ALNS)local search,ten kinds of damage and repair operators were designed,and the adaptive weighting mechanism was introduced,combined with the simulated annealing criterion to avoid falling into the local optimum.The performance of the BSO_ALNS algorithm was tested by selecting C,R,CR and other types of instances of different scales in Solomon.Taking the shortest path distance as the goal,the error between the BSO_ALNS algorithm solution and the historical optimal solution is within 1.5%.With the goal of minimizing the total cost of vehicle transportation,the optimal solution is obtained BSO_ALNS compared with BSO and ALNS.It is proved that the proposed algorithm can effectively solve the problem of urban low-carbon logistics path optimization.

关 键 词：BSO_ALNS算法 CVRPTW 低碳物流 路径优化 

分 类 号：U492.2[交通运输工程—交通运输规划与管理]