考虑客户重要度的软时间窗车辆路径优化问题研究
发布时间：2019-04-26 13:35
【摘要】：随着科技的不断进步和市场竞争的日益激烈化，降低物流费用作为“第三利润源”成为了企业最重要的竞争领域并受到人们的高度重视。占据了高比例物流费用的物流配送是物流系统中的关键环节，各企业一直致力于降低物流配送的成本，这也是现代物流领域研究的热点。物流配送涉及车辆路径问题(Vehicle Routing Problem，VRP)优化研究，意在以一个最优的成本在满足客户要求的条件下完成配送。客户具有时间窗限制的车辆路径问题就是带时间窗的车辆路径问题(Vehicle Routing Problem with TimeWindows,VRPTW)。传统的VRPTW是在满足客户需求量的前提下，考虑运输成本和由违反时间窗产生的惩罚成本，研究使运输成本和惩罚成本总和最低的最佳车辆行驶路径，但未考虑到客户满意程度对企业的影响。一些对企业具有重要意义的客户若未能得到令其满意的服务，则会降低该部分客户为企业带来的价值，给企业带来了一定的损失。 本文介绍了车辆路径问题和客户重要度的相关理论，根据大客户价值理论，对客户的潜在价值进行了分析，用客户的潜在价值来衡量客户的重要度。如果客户感到满意，则该客户的潜在价值可以发挥到最大，即为企业带来的利润就越大。以客户的潜在价值的总和来体现客户的满意情况，基于传统的车辆路径优化模型，研究构建使运输成本和时间惩罚成本构成的总运输成本最小同时使所有客户的潜在价值最大的软时间窗车辆路径模型。对于本文建立的考虑客户重要度的软时间窗车辆路径优化模型，采用小规模数据检验其正确性，同时与不考虑客户重要度的VRPTW模型进行比较，，突出本文所建模型的实用性。最后设计了一种改进的遗传算法进行求解，采用Solomon标准数据进行数值实验，实验结果进一步表明本文所建模型的准确性和算法的有效性。
[Abstract]:With the continuous progress of science and technology and the increasingly fierce competition in the market, reducing logistics expenses as the "third profit source" has become the most important competitive field of enterprises and has been highly valued by people. Logistics distribution, which occupies a high proportion of logistics expenses, is a key link in the logistics system. Every enterprise has been committed to reducing the cost of logistics distribution, which is also a hot spot in the field of modern logistics research. Logistics distribution involves vehicle routing problem (Vehicle Routing Problem,VRP) optimization, which aims to complete the distribution at an optimal cost under the condition that the customer's requirements are met. The vehicle routing problem with time window is the vehicle routing problem with time window (Vehicle Routing Problem with TimeWindows,VRPTW). The traditional VRPTW is to consider the transportation cost and the penalty cost caused by violating the time window under the premise of satisfying the customer's demand, and to study the best vehicle driving route which makes the sum of the transportation cost and the penalty cost lowest. However, the impact of customer satisfaction on the enterprise has not been taken into account. If some customers who are of great importance to the enterprise fail to get satisfactory service, they will reduce the value that these customers bring to the enterprise and bring some losses to the enterprise. This paper introduces the theory of vehicle routing problem and customer importance, analyzes the potential value of customers according to the theory of customer value, and measures the importance of customers by the potential value of customers. If the customer is satisfied, the potential value of the customer can be maximized, that is, the greater the profit for the enterprise. Based on the traditional vehicle routing optimization model, the customer's satisfaction is reflected by the sum of customer's potential value. A soft time window vehicle routing model is constructed to minimize the total transportation cost of transportation cost and time penalty cost and maximize the potential value of all customers. For the soft-time window vehicle routing optimization model which considers the importance of customers, small-scale data is used to verify the correctness of the model. At the same time, the model is compared with the VRPTW model which does not consider the importance of customers, which highlights the practicability of the model in this paper. Finally, an improved genetic algorithm is designed to solve the problem. Numerical experiments are carried out with Solomon standard data. The experimental results further show the accuracy of the proposed model and the effectiveness of the algorithm.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U492.22
本文编号：2466112
[Abstract]:With the continuous progress of science and technology and the increasingly fierce competition in the market, reducing logistics expenses as the "third profit source" has become the most important competitive field of enterprises and has been highly valued by people. Logistics distribution, which occupies a high proportion of logistics expenses, is a key link in the logistics system. Every enterprise has been committed to reducing the cost of logistics distribution, which is also a hot spot in the field of modern logistics research. Logistics distribution involves vehicle routing problem (Vehicle Routing Problem,VRP) optimization, which aims to complete the distribution at an optimal cost under the condition that the customer's requirements are met. The vehicle routing problem with time window is the vehicle routing problem with time window (Vehicle Routing Problem with TimeWindows,VRPTW). The traditional VRPTW is to consider the transportation cost and the penalty cost caused by violating the time window under the premise of satisfying the customer's demand, and to study the best vehicle driving route which makes the sum of the transportation cost and the penalty cost lowest. However, the impact of customer satisfaction on the enterprise has not been taken into account. If some customers who are of great importance to the enterprise fail to get satisfactory service, they will reduce the value that these customers bring to the enterprise and bring some losses to the enterprise. This paper introduces the theory of vehicle routing problem and customer importance, analyzes the potential value of customers according to the theory of customer value, and measures the importance of customers by the potential value of customers. If the customer is satisfied, the potential value of the customer can be maximized, that is, the greater the profit for the enterprise. Based on the traditional vehicle routing optimization model, the customer's satisfaction is reflected by the sum of customer's potential value. A soft time window vehicle routing model is constructed to minimize the total transportation cost of transportation cost and time penalty cost and maximize the potential value of all customers. For the soft-time window vehicle routing optimization model which considers the importance of customers, small-scale data is used to verify the correctness of the model. At the same time, the model is compared with the VRPTW model which does not consider the importance of customers, which highlights the practicability of the model in this paper. Finally, an improved genetic algorithm is designed to solve the problem. Numerical experiments are carried out with Solomon standard data. The experimental results further show the accuracy of the proposed model and the effectiveness of the algorithm.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U492.22
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