基于改进的蚁群算法求解多目标生鲜农产品车辆路径  

Solution for the Vehicle Route of Multi-objective Fresh Agricultural Products Based on the Improved Ant Colony Algorithm

作　　者：万杰[1] 耿丽 田喆 WAN Jie;GENG Li;TIAN Zhe(School of Economics and Management/Hebei University of Technology,Tianjin 300401,China;School of Artificial Intelligence and Data Science/Hebei University of Technology,Tianjin 300401,China)

机构地区：[1]河北工业大学经济管理学院,天津300401 [2]河北工业大学人工智能与数据科学学院,天津300401

出　　处：《山东农业大学学报（自然科学版）》2019年第6期1080-1086,共7页Journal of Shandong Agricultural University：Natural Science Edition

基　　金：河北省高等教育教学改革研究与实践项目(2017GJJG021)

摘　　要：针对考虑价格折扣的带时间窗的生鲜农产品车辆路径问题,用准时到达率和准时到达量表示客户服务质量,建立了以成本最低、服务质量最大和碳排放最少为目标的数学模型。并设计了一种改进的蚁群算法,即在启发因子中加入需求量和时间窗跨度因素,将目标权重加入到信息素的更新策略中,在完成一次迭代后再进行信息素的更新,加快了求解速度、提高了目标准确度,防止了最优解的局部优化。测试算例的结果表明:求解多目标生鲜农产品车辆路径问题时,与基本蚁群算法相比,改进的蚁群算法具有收敛速度快、目标准确度高等优点。Aiming at the vehicle routing problem of fresh agricultural products with time window considering price discount,we use the punctual arrival rate and punctual arrival volume to represent the customer service quality,establish a mathematical model with the lowest cost,the maximum service quality and the minimum carbon emission,and design an improved ant colony algorithm,that is,the requirement and time window span factors were added into the heuristic factor,the target weight was added into the pheromone update strategy,and the pheromone update was carried out after the completion of an iteration,which accelerated the solution speed,improved the target accuracy,and prevented the local optimization of the optimal solution.The test results show that compared with the basic ant colony algorithm,the improved ant colony algorithm has the advantages of fast convergence speed and high target accuracy when solving the vehicle routing problem of multi-objective fresh agricultural products.

关 键 词：蚁群算法 多目标优化 车辆路径 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]