工程硕士论文：基于云物流服务平台的车型配送方案改进分析.docx

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1、工程硕士论文：基于云物流服务平台的车型配送方案改进分析摘要：随着物联网、云计算及数据挖掘等技术与物流的结合，以云物流服务平台为核心的云物流服务模式是当前传统企业应对动态多变的市场竞争环境和提升自身竞争力的关键。摘要随着物联网、云计算及数据挖掘等技术与物流的结合，以云物流服务平台为核心的云物流服务模式是当前传统企业应对动态多变的市场竞争环境和提升自身竞争力的关键。配送是物流系统中极其关键的环节，随着越来越多的企业对定制化、专业化、个性化物流服务的需求，云物流服务模式下会产生大量的配送订单，车辆配送效率直接影响着企业的物流成本，对企业的生产经营会产生重大影响。所以，对云物流服务模式下的车辆配送优化

2、问题进行研究显得尤为重要。本文基于2017年合肥市包河区政府开展的科技成果转化项目一一云物流及其大数据服务关键技术研究及产业化，该项目构建了云物流服务模式平台，本文在云物流服务模式下以配送订单为中心对车辆配送优化问题开展带时间窗的多中心单车型配送优化问题和考虑取货需求的多中心多车型配送优化问题两方面的研究。首先，针对带时间窗约束的多中心单车型配送优化问题，考虑配送车辆在完成配送服务后返回不同配送中心的情况，采用k-means算法对云物流服务平台上配送订单进行聚类，建立了以行驶距离、时间惩罚成本、固定成本及剩余装载能力为目标的车辆配送优化模型，构建了基于Bellman-Ford的模型求解的改进遗

3、传算法，通过Bellman-Ford算法对遗传算法的染色体进行最优路径分割，在满足车辆容量等限制的情况下,获得多目标函数综合最优的配送方案。通过对实例进行结果分析，验证了模型及求解方法的可行性与适用性。其次，针对带时间窗约束的考虑取货需求时的多中心多车型配送优化问题，在取货需求的约束下建立了以行驶距离、时间惩罚成本、固定成本、配送剩余装载能力和取货剩余装载能力为目标的车辆配送多目标优化模型，构建了模型求解的遗传算法和模拟退火算法相结合的混合算法，结合实例数据，对求解算法进行仿真计算。通过结果分析与因素分析，验证了模型和求解方法的适用性，并分析了有无时间窗约束及不同目标函数对优化问题的影响。本文

4、的研究结果应用于云物流服务平台下车辆配送优化问题的决策中，对于降低云物流服务模式下的物流成本，提高车辆装载能力利用率有着重要意义，而且还有助于物流行业供给侧改革，推动物流行业和互联网深度融合，提高企业在动态多变的全球市场上的竞争力。关键词：云物流服务；车辆配送问题；取货需求；遗传算法。ABSTRACTWith the combination of technologies and logistics such asthe Internet of Things and cloud computing, the cloud logisticsservice model with the cloud

5、 logistics service platform as thecore is the key to the current traditional enterprises to cope withthe dynamic and changing market competition environment andenhance their own international competitiveness. Distribution isan extremely critical link in the logistics system. With more andmore enterp

6、rises demanding customized, specialized andpersonalized logistics services, the efficiency of vehicledistribution under the cloud logistics service model directlyaffects the logistics costs of enterprises and has an importantimpact on the production and operation of enterprises.Therefore, it is part

7、icularly important to study the optimization ofvehicle distribution under the cloud logistics service model.Thispaper is based on the 2017 Baohe District Government of HefeiCity carrying out a scientific and technological achievementstransformation project-Cloud Logistics and Key TechnologyResearch

8、and Industrialization of Big Data Services.This project builds a cloud logistics service model platform.Focusing on the delivery order, the vehicle distributionoptimization problem is researched on the multi-centersingle-model distribution optimization problem with time window andthe multi-center mu

9、lti-model distribution optimization problemconsidering the pickup demand.First, for the multi-center single-vehicle distribution optimization problem with time windowconstraints, considering the situation that the distribution vehiclereturns to different distribution centers after completing thedist

10、ribution service, the paper uses the k-means algorithm tocluster the distribution orders on the cloud logistics serviceplatform. The paper establishes a vehicle distributionoptimization model aiming at driving distance, time penalty cost,fixed cost and remaining loading capacity, and builds animprov

11、ed genetic algorithm based on Bellman-Ford modelsolution. The paper uses Bellman-Ford algorithm to pide theoptimal path of the genetic algorithms chromosomes, andobtains a multi-objective function comprehensive optimaldistribution plan when the vehicle capacity and other constraintsare met. By analy

12、zing the results of the examples, the feasibilityand applicability of the model and the solution method areverified.Secondly, in view of the multi-center and multi-vehicledistribution optimization problem when considering the demandfor pickup with time window constraints, the paper establishes avehi

13、cle distribution optimization model aiming at drivingdistance, time penalty cost, fixed cost and remaining loadingcapacity, and builds a hybrid algorithm combining geneticalgorithm and simulated annealing algorithm to solve the model,combined with example data to simulate calculation of thesolution

14、algorithm.Through the result analysis and factor analysis, theapplicability of the model and solution method is verified, andthe influence of time window constraints and different objectivefunctions on the optimization problem is analyzed.The researchresults of this paper are applied to the decision

15、 of vehicledistribution optimization under the cloud logistics serviceplatform, which is of great significance to reduce the logisticscost under the cloud logistics service model and improve theutilization rate of vehicle loading capacity.At the same time, itcan also contribute to the supply-side reform of the logisticsindustry, promote the deep integration of the logistics industryand the Internet, and improve the competitiveness of enterprisesin the dynamic and changing global market.KEYWORDS: Digital Workshop; Production Logistics;Material Storage; Genetic Algorithmo第一章绪论1.1.