Application of Robust Strategies in Location Selection of Logistics Distribution Center for Fresh Agricultural Products

In view of the uncertainty in the location selection of logistics distribution center for the fresh agricultural products,the present study established a robust model based on the maximization of principal component score taking budget cost parameters as an example.In the process of model solving,the interval form of the uncertain set was used to clarify the constraint conditions,to transform into a certain 0-1 integer linear programming model,so as to solve with the aid of LINGO software.Finally,through studying the location selection of logistics distribution center for fresh agricultural products in the Beijing-Tianjin-Hebei region,it analyzed the application of the robust model and tested the validity of the model.

Study on Location of Wuhan Steel Logistics Distribution Center

This paper studies the location of Wuhan steel logistics distribution center. First of all, according to Wuhan Iron and Steel Plant sales in Hunan Province and the relative position of the city, the transport costs are calculated from Wuhan Iron and Steel Plant to the demand point. We further analyze the necessity of establishing steel logistics distribution center, using the precise center of gravity to determine the actual location of the distribution center. After the establishment of distribution center, the total freight is reduced by 15.46 million yuan from Wuhan Iron and Steel Plant to each city in Hunan province via distribution center each year. The results of this paper can provide theoretical basis for the logistics node planning of related enterprises.

An Uncertain Programming Model for Competitive Logistics Distribution Center Location Problem

We employ uncertain programming to investigate the competitive logistics distribution center location problem in uncertain environment, in which the demands of customers and the setup costs of new distribution centers are uncertain variables. This research was studied with the assumption that customers patronize the nearest distribution center to satisfy their full demands. Within the framework of uncertainty theory, we construct the expected value model to maximize the expected profit of the new distribution center. In order to seek for the optimal solution, this model can be transformed into its deterministic form by taking advantage of the operational law of uncertain variables. Then we can use mathematical software to obtain the optimal location. In addition, a numerical example is presented to illustrate the effectiveness of the presented model.

Decision Support for DCs Location and Distribute Range Planning: Case Study in Asia-Pacific Region

A major concern is to locate manufacturing plants and/or distribution centers to serve the needs of consumers widely and rapidly with reasonably distributive cost and flexible delivery time. The purpose of this research is to help enhance a decision support for three distribution center locations with different ranges of area in order that the distributors are able to serve appropriately the retailers in twelve large cities or capitals of the nine countries in Asia-Pacific region. The researcher applies the theory of supply chain and logistics management systems with Excel, Visual Basic and Genetic Algorithms programs to find the research results. The research results revealed that the interfaces of Excel, Visual Basic, and Genetic Algorithms programs helped decision support in distribution range selection for 3 distribution center locations. Each of which had different ranges of area for distribution with a rapidly, flexibly distributed time. However, variables resulting in a decision-making should be adjusted under certain circumstances for more reliable, specific needs.

Distribution Locational Marginal Pricing Based Equilibrium Optimization Strategy for Data Center Park with Spatial-temporal Demand-side Resources

This paper proposes a distribution locational marginal pricing(DLMP) based bi-level Stackelberg game framework between the internet service company(ISC) and distribution system operator(DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads(IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

Location optimization of multiple distribution centers under fuzzy environment

Locating distribution centers optimally is a crucial and systematic task for decision-makers.Optimally located distribution centers can significantly improve the logistics system's efficiency and reduce its operational costs.However,it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center.With growing logistics demands,multiple distribution centers become necessary to meet customers' requirements,but few studies have tackled the multiple distribution center locations(MDCLs) problem.This paper presents a comprehensive algorithm to address the MDCLs problem.Fuzzy integration and clustering approach using the improved axiomatic fuzzy set(AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria.Then,technique for order preference by similarity to ideal solution(TOPSIS) is applied for evaluating and selecting the best candidate for each cluster.Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure.Results from a case study in Guiyang,China,reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection.This new method may easily be extended to address location planning of other types of facilities,including hospitals,fire stations and schools.

基于改进K-means算法的物流配送中心选址研究

针对传统K-means算法需要主观设定K值及无法处理类别型数据问题,文章运用肘部法及轮廓系数法确定合理K值,对类别型数据采取独热编码(One-Hot Encoding)转换为可以处理的连续型数据,并将其运用到在物流配送中心选址中;并综合考虑多种类别的影响因素,构建了相应的影响因素指标体系,提出的模型能够识别输入数据的数值型及类别型数据,实现样本的有效聚类。相关的案例分析结果表明,相比传统K-means聚类,文章的改进K-means算法选址结果可使物流总成本降低8.76%,运营成本降低14.85%,固定成本降低8.09%,效果显著。

生鲜农产品车辆和无人机组合配送路径优化及效果测试

【目的】为提升生鲜农产品的配送效率,提出生鲜农产品车辆与无人机组合配送路径优化方案。【方法】按照生鲜农产品冷链配送体系要求,采用模糊K-means聚类方法确定生鲜农产品配送中心;定义配送中心位置为车辆和无人机组合配送的起点,以生鲜农产品配送总成本最小为目标函数,利用蚁群算法和无人机物流车协调配送算法求解上述构建的目标函数,获取最低配送总成本的配送路径规划结果;引入安全启发函数优化蚂蚁转移规则,获取安全最高的无人机最优配送路径。再按照该方法,将生鲜农产品按照质量划分为重件和轻件,并计算重件和轻件在配送过程中产生的成本,获取在不同的总配送生鲜农产品快件数量下,优化前与优化后的配送成本。【结果】效果测试表明,该方法具有良好的聚类效果,簇类凝聚度(Davies-Bouldin, DB)指数值均低于0.15,可合理选取配送中心,保证配送中心的覆盖程度。并且通过对比该方法应用前后的数据对比可知,应用前重件和轻件的配送成本明显更高于应用后的成本。【结论】该方法可以更高质量地完成车辆和无人机组合配送路径规划,能够显著降低生鲜农产品的配送总体成本,应用效果满足应用需求,提升配送效率。

基于最小权覆盖的医药电商配送中心选址及区域覆盖优化研究

配送中心选址及区域划分是物流配送过程中的关键环节,直接决定了配送时效及配送成本,在当今电子商务领域显得尤为重要。本文针对国内医药电商企业,提出了一种考虑药品配送时效的配送中心选址策略;随后建立该问题的整数规划模型,采用最小权顶点覆盖方法描述问题,并通过优先队列分支限界算法对此模型进行求解,得出最优选址结果;最后按最小运费原则将被重复覆盖区域进行再划分,得到配送中心选址及区域划分最终方案。本文基于上述策略为国内某头部医药电商企业提供了两种选址方案:保留企业原有配送中心并确定新配送中心选址点(改进选址方案)和从企业所有需求节点中重新为配送中心选址(重选址方案),并使用企业真实销量和物流数据进行算例分析。

灾后应急配送中心选址问题的降阶回溯算法

近年来自然灾害频发,提高灾后应急物资的配送效率一直以来都是性命攸关的大事,因此文章基于灾后应急配送中心选址问题的精确算法进行研究。首先,建立该问题的数学模型并对该问题中的数学性质进行研究和证明;其次,在这些数学性质的基础上,设计上下界子算法和降阶子算法,这些子算法能够有效减少解空间,提高算法的效率,使该算法能够更有效地处理规模更大的问题;再次,提出降阶回溯子算法,通过剪枝和局部降阶进一步缩小问题的搜索规模,并能得到最优解;最后,通过分析和解决一个案例来更清楚地说明算法的原理和操作步骤。

基于改进布谷鸟算法的物流配送中心选址研究

近年来,物流行业不断发展壮大,配送中心作为物流系统中承上启下的关键节点,在减少物流配送成本、提高服务响应效率上具有十分重要的战略价值。针对供应链环节中因多源多点造成的配送成本增加、库存周转率低等痛点,文中提出了一种改进的布谷鸟算法用于解决物流配送中心选址问题。该算法在传统的布谷鸟算法的基础上,引入粒子群优化算法,将两者有效地组合起来进行求解,并在求解过程中增加自适应参数,以避免出现局部最优现象,增加全局搜索能力。实验表明,相较于其他启发式算法,在解决配送中心选址问题上,改进布谷鸟算法能够有效降低物流配送成本,加快求解速度,提高库存周转次数,为企业物流的预算方案提供参考依据。

基于重心法的配送中心选址问题研究——以天友乳业公司为例

采用重心模型法对重庆天友乳业公司的物流配送中心选址进行分析,以得到物流配送中心的优化选址结果。通过选择合适的初始位置和迭代优化的方式,最终得出结论:重庆市沙坪坝区白杨沟东南一千米左右为模型最佳地址(106.562 0,29.525 0),即配送中心经纬度。基于经济分析方法,建立天友乳业物流配送中心优选模型的相关做法,希望为相关企业在物流网络空间规划和选择上提供更多参考。

改进麻雀搜索算法求解物流配送中心选址问题

为提升物流配送效率,降低配送成本,提出一种改进麻雀搜索算法M-SSA求解物流配送中心选址问题。在基本麻雀搜索算法SSA中,设计均匀化Logistic映射机制提升初始种群的均匀性和随机性;利用正余弦优化和惯性权重机制改进发现者位置更新,提升全局搜索能力;引入柯西混沌变异机制增强种群多样性,避免局部最优解。利用M-SSA算法求解物流配送中心选址问题。实验结果表明,在解决配送中心选址问题上,M-SSA算法可以降低物流配送成本,提升配送效率。

基于聚类-粒子群算法的冷链物流配送中心选址分析

针对冷链市场需求不断扩大,冷链物流产业却无法快速升级的情况,终端配送企业为了提高企业效益,需要在保证满足客户需求的同时,构建以成本最小化为目标的冷链配送中心选址模型。为解决上述问题,首先使用Mean-shift聚类算法对未知形状、未知特征的需求点进行处理,确保配送中心选址在空间分布上的合理性,同时降低运算难度;其次引入反向搜索、变异和交叉策略对传统粒子群算法(PSO)进行改进,使用改进粒子群算法(IPSO)对模型进行求解。相关实例分析结果表明,使用IPSO算法的选址总成本相比PSO算法减少11.4%,迭代次数相比减少28.6%,验证了IPSO算法可以得到质量更高的解,有效节省了选址成本。

求解物流配送中心选址问题的改进鸽群算法

为了提高鸽群优化算法求解物流配送中心选址问题的优化效果,减少物流配送成本,提出了一种改进的鸽群优化算法。该算法在基础鸽群优化算法上,引入灰狼优化算法在寻优过程中的捕食策略,能够有效地提高鸽群优化算法的局部搜索能力、增强算法的寻优性能。由函数测试实验可得,该算法在求解测试函数最优值上具有寻优速度快、收敛精度高的特点。最后,将其应用到求解物流配送中心选址问题中,实验结果表明:改进的鸽群优化算法更适合求解高维物流配送中心选址问题。

基于改进麻雀搜索算法的配送中心选址研究

针对物流配送中心选址不合理导致配送效率低下的问题,采用萤火虫扰动策略改进麻雀搜索算法,构建二级物流配送中心网络结构,解决配送中心选址问题。采用手肘法确定最优二级配送中心数量,将运输成本、运输时间和配送时间三项成本之和最小值作为目标函数,增加运送车辆限制等约束条件,获得合理的配送中心地址和最佳成本。以吉林省某区县小区实际情况为例,将改进后的麻雀搜索算法与常规的麻雀搜索算法进行对比,结果表明:改进后的麻雀搜索算法可将总成本降低16.67%,配送中心选址更加合理有效。

铁路多地多中心监控运维关键技术研究

为适应中国国家铁路集团有限公司统筹规划及数据中心多地多中心的发展要求,实现全国铁路数据中心集中运营维护(简称:运维)管理的目标,提出多地多中心监控运维体系架构。采用监控运维平台分布式部署、资产统一管理、统一监控等关键技术,实现了分钟级的批量多云环境巡检,以及对监控数据、日志数据的统计分析,并对云环境的资源使用提出了优化建议,以期为铁路数据中心云环境的运维工作提供重要支撑。

建筑供应链物资物流配送中心动态选址仿真研究

为有效提升选址精度,提出建筑供应链物资物流配送中心动态选址方法。分析影响选址的因素,构建备选点初选评价指标体系,对备选点实施排序处理,并剔除无用备选点,完成备选点位置初选;基于初选结果采用GIS技术建立物流配送中心动态选址模型,通过多目标粒子群算法求解该模型,根据求解结果完成物流配送中心的动态选址。实验结果表明,使用该方法开展配送中心动态选址时,选址时间短、精度高。

基于系统聚类和重心法的新能源汽车配送中心选址分析

在国家大力提倡低碳经济、绿色发展的背景下,具有节能环保、低碳排放等优点的新能源汽车行业得到快速发展。针对新能源汽车快速发展中面临的配送中心选址问题,构建了一个基于系统聚类和重心法相结合的选址模型并给出求解方法。通过实例分析得出:在新能源汽车进行多个设施选址时,采用系统聚类法能够合理快速的实现选址样本的分组,明确选址的数量;各组分别采用单重心法保证了每个选址中心到各个服务区域的配送成本最小;建成了有2个制造厂、4个配送中心和若干需求点组成的二级物流网络。同时,建议配送中心在选址前,明确选址影响因素和合适的选址方法;在选址中,明确选址流程,确保选址符合实际;在选址后,重点关注整个配送中心网络的管理。

农产品企业物流配送中心选址评价决策研究

农产品企业物流配送中心的选址问题已经逐步成为约束现代农业物流发展的主要因素之一。综合各种因素对农产品企业物流配送中心选址进行客观评价,提供科学的决策是疏通农产品物流配送环节的重要问题。文中以农产品企业物流配送中心选址为研究对象,采用解释结构法与层次分析法相结合的方式,首先将选址工作的经济成本、周边环境、自然因素以及国家有关政策法规等一系列因素加以考察、完善和细化,通过ISM模型进行研究,并构造出层次分析结构图,再利用层次分析法给出了各层级指标的权重,最终利用一个典型农产品企业——德农企业的农产品配送中心选址的实际情况进行了实证分析,利用文中提出的方法进行了计算,并给出了配送中心选址的优先顺序以及相关建议,通过实证分析表明了该方法的科学性和有效性。