Yong Wang , Mengyuan Gou , Siyu Luo , Jianxin Fan , Haizhong Wang
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Combined with an elite iteration mechanism to prevent the proposed algorithm from falling into search stagnation and improve the convergence performance. The superiority of the proposed algorithm is verified by comparing with CPLEX solver (i.e., ILOG CPLEX Optimization Studio 12.10), multi-objective ant colony optimization, multi-objective particle swarm optimization, multi-objective evolutionary algorithm, multi-objective genetic algorithm, and decomposition-based multi-objective evolutionary algorithm with tabu search. Besides, the proposed model and algorithm are tested by a real-world case study in Chongqing city, China, and the further analysis indicates that significant improvement can be achieved. Furthermore, by incorporating the recognition and prediction techniques of artificial intelligence on dynamic demand data, the proposed approach can realize the self-optimization of multi-depot vehicle routes and the precise allocation of logistics resources in dynamic environments. 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引用次数: 0
摘要
城市物流回收行业的快速发展,加上取货和送货网络的复杂性,造成了客户需求的动态激增,加剧了物流资源共享的难度。因此,本研究将重点放在具有时间窗口和动态需求的多网点取货和送货车辆路由问题上,并将资源共享纳入其中。本文建立了一个双目标数学模型,以最小化总运营成本和车辆数量。结合三维亲和传播聚类和自适应非支配排序遗传算法-II,找到帕累托最优解。提出了一种动态需求插入策略,以确定动态情况下的车辆服务序列。结合精英迭代机制,防止算法陷入搜索停滞,提高收敛性能。通过与 CPLEX 求解器(即 ILOG CPLEX Optimization Studio 12.10)、多目标蚁群优化、多目标粒子群优化、多目标进化算法、多目标遗传算法以及基于分解的多目标进化算法与 tabu 搜索进行比较,验证了所提算法的优越性。此外,在中国重庆市的实际案例研究中对所提出的模型和算法进行了测试,进一步的分析表明可以实现显著的改进。此外,通过结合人工智能对动态需求数据的识别和预测技术,所提出的方法可以实现动态环境下多网点车辆路线的自我优化和物流资源的精确分配。这项研究有利于构建数字化智能城市物流系统。
The multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands
The rapid development of the urban logistics recycling industry, combined with the complexity of the pickup and delivery networks, has created a surge in dynamic customer demands and exacerbated the difficulty of logistics resource sharing. Accordingly, this work focuses on a multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands, which incorporates resource sharing. A bi-objective mathematical model is formulated to minimize the total operating cost and number of vehicles. A three-dimensional affinity propagation clustering and an adaptive nondominated sorting genetic algorithm-II are combined to find Pareto optimal solutions. A dynamic demand insertion strategy is proposed to determine the vehicle service sequences for dynamic situations. Combined with an elite iteration mechanism to prevent the proposed algorithm from falling into search stagnation and improve the convergence performance. The superiority of the proposed algorithm is verified by comparing with CPLEX solver (i.e., ILOG CPLEX Optimization Studio 12.10), multi-objective ant colony optimization, multi-objective particle swarm optimization, multi-objective evolutionary algorithm, multi-objective genetic algorithm, and decomposition-based multi-objective evolutionary algorithm with tabu search. Besides, the proposed model and algorithm are tested by a real-world case study in Chongqing city, China, and the further analysis indicates that significant improvement can be achieved. Furthermore, by incorporating the recognition and prediction techniques of artificial intelligence on dynamic demand data, the proposed approach can realize the self-optimization of multi-depot vehicle routes and the precise allocation of logistics resources in dynamic environments. This study is conducive to the construction of a digitally-intelligent urban logistics system.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.