基于蚁群系统算法的车辆路径问题研究

通过引入改进的蚂蚁算法--蚁群系统算法来解决基本的车辆路径问题。针对具体实例运用计算机编程得出了基于蚁群系统算法的最优解,并与其它两种启发式算法进行对比,指出了蚁群系统算法的优势与存在的不足,提出了进一步研究的方向。     

车辆路线问题的平行节约启发式算法

在分析了车辆路线问题特征的基础上，建立了数学模型，并给出了平行节约启发式算法。     

基于遗传算法(GA)的配送路径优化问题研究

文章在建立配送车辆路径优化问题数学模型的基础上，构造了遗传算法来求解该问题，并在算法中引入了自然选择、交叉操作、变异操作等思想：实验结果表明，此算法可以有效求得车辆路径问题的优化解或近似优化解．是求解车辆路径问题的一个较好的方案。     

基于遗传算法的多周期确定性IRP问题研究

本文主要通过在分析多周期确定性条件的基础上建立的数学模型,研究多周期确定性客户需求下的IRP问题,并利用遗传算法对模型进行求解.同时以小群体规模、指定交叉率和变异率为实例,进行数值模拟实例分析.与利用经济批量采购公式求解的客户补货量进行了对比分析,遗传算法可以有效地解决了IRP中最优补货量的问题.     

基于车底配置最优的地铁列车交路设计

分析地铁列车交路设置的影响因素,给出任一交路下车底数下界的数学计算公式。通过建立最大程度满足客流需求的最优车底配置模型,以及模型的求解步骤,实现了以车底数最优作为标准的多种地铁列车交路方案的比选。     

生态旅游区废弃物回收的逆向物流选址-路径问题

研究了生态旅游区废弃物逆向物流网络设计问题,包括中转站选址和车辆路径问题,并建立了最小化选址费用和运输费用的模型。选址路径问题为NP困难问题,本文采用了四叉树原理划分满足车辆容量限制的收集区,将问题化为小型的TSP问题。     

仓储中心拣选作业研究综述

在仓储中心拣选作业中,以降低人员行走距离、提高拣选效率的研究为对象,对拣选作业中的相关研究问题和解决方案进行了讨论和综述。     

基于混合粒子群算法的车辆路径优化问题研究

设计了一种引入了量子和遗传算法思想的粒子群算法．该算法结合了粒子群优化算法的快速寻优能力和量子算法可以同时处理多个目标的优点．避免了基本粒子群算法易陷入局部最优的缺点,提高了求解速度。该算法用于解决车辆路径问题。通过实验表明了这种算法具有较好的性能。     

Vehicle flow formulation for two-echelon time-constrained vehicle routing problem

Two-echelon routing problems, including variants such as the two-echelon vehicle routing problem (2E-VRP) and the two-echelon location routing problem (2E-LRP), involve assignment and location decisions. However, the two-echelon time-constrained vehicle routing problem (2E-TVRP) that caters to from-linehaul-to-delivery practices does not involve assignment decisions. This routing problem variant for networks with two echelons has not yet attracted enough research interest. Localized or long-distance services suffer from the lack of the assignment decisions between satellites and customers. Therefore, the 2E-TVRP, rather than using assignment decisions, adopts time constraints to decide the routes on each of the two interacting echelons: large-capacity vehicles transport cargoes among satellites on the first echelon, and small-capacity vehicles deliver cargoes from satellites to customers on the second echelon. This study introduces a mixed integer linear programming model for the 2E-TVRP and proposes a heuristic algorithm that incorporates the savings algorithm followed by a variable neighborhood search phase. Illustrative examples are used to test the mathematical formulation and the heuristic and a case study is used to demonstrate that the heuristic can effectively solve realistic-size instances of the 2E-TVRP.     

THE LAST MILE CHALLENGE: EVALUATING THE EFFECTS OF CUSTOMER DENSITY AND DELIVERY WINDOW PATTERNS

Consumer direct delivery of packages ordered over the Internet has grown at well over 25 % per year over the past 10 years and now accounts for over $100 billion in sales in the U.S. alone. Retailers have rushed to capitalize on what has commonly been labeled multi‐channel retailing, while logisticians have faced a challenge in devising efficient methods of delivering billions of packages to customer homes. Inefficient deliveries in this “last mile” of the supply chain have led to numerous business collapses as well as a substantial increase in delivery costs. We present a study which examines the effect of two factors (customer density and delivery window length) on the overall efficiency of the delivery route. Data are collected based on empirically derived settings from interviews with several practicing managers. Results provide insight for logistics and marketing managers who must balance customer desires for convenience with business desires for efficiency. The data show that offering a 3 hour delivery window is 30–45% more expensive than offering unattended (9 hour delivery window) delivery. The results provide a tool for managers to address the tradeoffs between various settings for the independent variables (customer density and delivery window length) and the overall route efficiency.