摘要
物流网络优化中的车辆路径问题和设施选址问题是物流系统研究中的重要组成部分,其无论是在国民经济生活中还是在军事战争领域里都有着重要的理论意义和实用价值。以往的网络优化问题的研究主要是集中于静态信息以及确定性领域,即使考虑到不确定因素,也是一类比较简单的问题。而在实际中,涉及大量的不确定信息以及复杂的约束条件,传统的模型难以描述随机条件下或动态条件下的物流网络优化问题;而且,随着网络规模的扩大,使得物流网络优化问题求解变得越来越困难。因此,有必要进一步研究在随机条件下以及动态条件下的物流网络优化问题,并为问题求解构造出更有效、更符合实际的模型与算法。本文针对相关的随机条件下以及动态条件下的物流网络优化问题,给出了相应的模型及算法,并进行了应用计算。主要的研究内容及创新点如下:
随机需求下带时间窗的物流网络车辆路径优化研究。考虑用户需求随机以及对客户服务有时间窗口限制的情况下,提出了一种随机需求下带时间窗物流网络车辆路径优化问题研究。分析了问题的特性,给出了预优化求解策略,建立随机补偿模型,并对问题求解中的期望额外总费用计算进行了详细的讨论。针对研究问题,设计了一种自适应大邻域启发式搜索求解算法。通过对构造的56组示例的计算,并与另一种“确定性求解策略”进行的对比,验证了构建模型及设计算法的有效性,同时也对子算法性能进行了评估。
随机需求下可拆分服务的物流网络车辆路径优化研究。针对随机需求下,客户需求可拆分服务的情况,提出了一种随机需求下可拆分服务物流网络车辆路径优化问题研究,给出了一种需求可拆分服务的配对车辆回归求解策略,建立了数学模型。在对问题求解中的期望回归费用计算时,分别对配对车辆的不协作与协作的情况进行了讨论。针对问题求解,设计了一种大邻域启发式搜索算法。最后,通过对设计的应用示例的计算,以及与需求不允许拆分服务情况下的结果对比表明,当客户平均需求量大于一半车辆容量时,允许需求可拆分服务的随机需求下的车辆路径优化结果明显优于需求不可拆分服务的随机需求下的车辆路径优化结果。
随机服务时间下的物流网络车辆路径优化研究。考虑确定的车辆行驶时间、随机的车辆服务时间以及拥有最大工作时间限制的情况下,提出了一种随机服务时间下物流网络车辆路径优化问题研究。针对问题特性,建立了相关数学模型,讨论了期望费用的计算。设计了一种G型变邻域启发式搜索算法以求解问题。通过应用示例计算,将设计的算法与给出的另外两种求解算法所获得的结果进行了对比,同时测试了不同参数设置下的对应用示例计算结果的影响,获得了比较好的效果。
保障网络动态选址与分配研究。针对军事背景下,被保障单元需求呈现多周期动态变化的情况,研究了保障网络中动态选址与分配优化问题。建立了数学模型,并设计了一种混合式进化算法。通过对设计的一组应用示例的计算,对模型及算法进行了有效验证。
The researches on the vehicle routing problems and the facility location problems are very important parts of the logistics networks optimization. These two classes of problems are significant on the economic area and military area. In the classical logistics networks optimization, the researchers mainly focused on the deterministic problems. The problems are very simple, even though they considered the uncertainty elements. In the real life, there exist much uncertainty information and many complex constraints. Traditional models and algorithms show great limitations in solving the stochastic problems and dynamic problems. Therefore, it is necessary to do further research jobs to construct more effective models and algorithms for the stochastic logistics networks optimization and the dynamic logistics networks optimization. In this thesis, a series of stochastic vehicle routing problems and a dynamic location allocation problem are studied thoroughly. The main contributions of this thesis are outlined as follows:
Firstly, the research of the vehicle routing problem with stochastic demands and time windows (VRPSDTW) is provided. In this problem, the demands of customers are stochastic and there exists a time window for each customer. A priori optimization strategy is provided for the problem, and a stochastic programming with recourse model is built. The computation of the expected cost of recourse is complex and is discussed in detail. An adpative large neigborhood search heuristic is developed for the solving of problem. The heuristic is tested on the modified Solomon instances. The computational results are compared with the results which are obtained by an alternative solving strategy. The effectiveness of the sub-heuristics is also evaluated.
Secondly, the research of the vehicle routing problem with stochastic demands and split deliveries (VRPSDSD) is provided. A paired-vehicles recourse with split delivery strategy is given for the solving problem. The mathematical model is then built. The cooperations between the two paired vehicles and the non-cooperative operations are respectively considered when the expected cost of recourse is computed. A large neigborhood search heuristic is devised, and the heuristic is tested on the modified Solomon instances. The results show that when the average customer demand is larger than the half capacity of vehicle, the savings with the paired-vehicles recourse with split delivery strategy are obvious.
Thirdly, the research of vehicle routing problem with stochastic service times (VRPSTT) is provided. In this problem, the customer demand and the travel time are deterministic, but the service times on the customers are stochastic. There also exists a limit on the route duration. The mathematical model is constructed and the expected cost of solution is discussed. The general variable neighborhood search heuristic is provided for the solving. In the experiments, the general variable neighborhood search heuristic is compared with two other heuristics. Also the computations are tested with different parameters. The computational results verify the performance of model and algorithm.
Fourthly, a dynamic location allocation problem is studied. With a military background, the supported unit is associated with the multiple periodical demand. The mathematical model is constructed and a hybrid evolutionary algorithm is developed for the problem. The model and algorithm are verified by the experimental results.
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