顾客时间窗变化的物流配送干扰管理模型及其算法
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摘要
尽可能满足顾客需求是每一家物流企业提升服务质量、提高市场竞争力的重要手段。然而现实中,顾客可能会因各种突发事件而无法在约定的时间赶到约定的地点等候配送服务,从而不得不在物流配送活动已经启动之后,临时变更他们的配送服务时间窗。顾客需求的这种变化不仅不可避免,而且难以预测,给物流企业的“随需应变”目标带来了极大的挑战。这时,物流企业虽然希望尽量满足顾客的要求,但也同时需要考虑自己的配送能力限制和系统的整体扰动,在兼顾系统扰动和顾客满意度等因素的基础上给顾客一个明确的答复。
为此,本文针对顾客时间窗变化的物流配送干扰管理问题,以提高物流配送干扰管理的科学性为目标,构建顾客时间窗变化的干扰管理模型,设计相应的求解算法,通过算例及应用实例验证模型及其算法的有效性,为物流企业解决顾客时间窗变化的问题提供理论方法支持。本文的主要研究工作如下：
(1)顾客时间窗变化的干扰管理问题分析
分析物流企业针对顾客时间窗变化事件的典型处理流程,将整个处理流程提炼为顾客与物流企业之间的一个多阶段的协商过程,进而针对这一过程中“顾客变更时间窗后如何调整行车路线,以使系统扰动最小化”、以及“一旦顾客变更后的时间窗对系统扰动较大,物流企业无法接受,则如何在新时间窗附近再次向顾客推荐一个可行的时间窗”这两个问题提出处理思路,为问题的建模与求解奠定基础。
(2)顾客时间窗变化的干扰管理模型研究
从顾客、车辆驾驶员、物流企业三个方面衡量顾客时间窗变化对物流配送系统产生的扰动程度,建立“顾客时间窗变化的车辆路线调整模型”、“新时间窗推荐模型”这两个干扰管理子模型,并进而形成多阶段的顾客时间窗变化的干扰管理模型。
(3)顾客时间窗变化的干扰管理模型的求解算法研究
针对“顾客时间窗变化的车辆路线调整模型”,以干扰事件发生时的问题状态为基础,采用二维染色体结构,以及适合多目标决策模型的适应值计算方法,设计出用于求解该类模型的一种遗传算法；同时提出该模型的启发式求解算法,该算法基于新车增派和多车协作的启发式知识,针对受扰车辆路线上的所有顾客,依次将每一顾客在原计划的配送车辆、其他在途车辆、以及增派的新车之间分配；针对“新时间窗推荐模型”,在受扰车辆的未服务顾客的排序结果中调整变更时间窗的顾客的前后顺序,并利用启发式救援算法计算系统扰动程度能够容忍的一个未服务顾客排序结果,进而对顾客服务时间进行松弛,得到可推荐的新时间窗。
(4)顾客时间窗变化的物流配送干扰管理应用研究
运用标准算例验证求解算法的有效性：并以京东物流在大连市的快件配送为应用背景,针对实际的顾客时间窗变化事件,运用本文的干扰管理模型及其求解算法进行问题处理,验证本文所提方法的有效性。
本研究针对顾客时间窗变化这一物流配送干扰管理难题进行了有益的探索,有利于提高物流配送干扰管理的科学性、有效性,改进目前物流企业所普遍存在的落后的干扰管理手段,增强物流配送干扰管理系统的鲁棒性,使物流配送干扰管理的理论与应用研究朝着物流企业与顾客相互协商的柔性化处理方向更进一步。
To meet customer needs as much as possible is an important means of improving the service quality and enhancing market competitiveness for every logistic enterprise. But in reality, customers may not be able to get to the given place at the given time as a result of accidental events. Therefore, they have to change their time windows of logistic services even if the delivery vehicles are running. It is a great challenge for logistic enterprises that the customer time window changes are not only inevitable but also hard to predict. Although all logistic enterprises wish to meet customers'demands, they have to consider their logistic capacities and system disruptions. They also have to answer customers based on the considerations of system disruptions and customers'satisfactions.
This paper focuses on the disruption management problem of customer time window changes. A disruption management model for customer time window changes is studied and its algorithm is designed, which are proved by application instances. This paper will present theory and methodology supports for logistic enterprises to solve the problems of customer time window changes. The main research work is summarized as follows:
(1) Problem analysis
A typical disruption management process of logistic enterprises is analyzed for customer time window changes in distribution. The process is concluded as a multi-stage discussion procedure between customers and logistic enterprises. Two problems that occurred in the procedure are discussed. The first problem is the changing of vehicle routes when customers have changed their time windows in order to minimize the system disruption. The second problem is to recommend a feasible time window which is nearest to the changed time window of a customer when the logistic enterprise can not accept the system disruption. The analysis of the disruption management problem forms the basis of modeling and solving the problem.
(2) Research on the disruption management model for customer time window changes
The disruption that the customer time window changes bring to the distribution system is measured by the three aspects of customers, vehicle drivers, and logistic enterprises. Two models are constructed:one is the vehicle route changing model after a customer time window changes, and the other is the new time window recommendation model. A multi-stage disruption management model for customer time window changes is made based on the above two models.
(3) Research on the disruption management algorithm for customer time window changes
A genetic algorithm is designed to solve the vehicle route changing model. In the algorithm, a two-dimension chromosome structure and a fitness function that fits multi-objective decision model are presented. A heuristic algorithm is also presented to solve the model. The heuristic iteratively assigns each disrupted customer to its old vehicle, other running vehicles, or an idle vehicle. To solve the new time window recommendation model, the sequence of the non-served customers of the disrupted vehicle is changed; and the best one will be chosen by the heuristic algorithm according to the system disruption. The customer time window of the best service sequence will be relaxed in order to obtain a recommended time window.
(4) Application research on the disruption management problem for customer time window changes in distribution
Benchmark instances are used to test the validities of the algorithms. The presented disruption management model and algorithm are also used to deal with the customer time window changes in the express and distribution of JingDong Logistics in order to test their validities.
This paper contributes to the exploration of the disruption management problem for customer time window changes in distribution. It helps to increase the instantaneity and the rationality of the disruption management in distribution and improve the old disruption management method in most logistic enterprises. The results should enhance the robustness of the disruption management system in distribution, and promote the theory and application research of disruption management towards the flexible interactions between logistic enterprises and customers.
为此,本文针对顾客时间窗变化的物流配送干扰管理问题,以提高物流配送干扰管理的科学性为目标,构建顾客时间窗变化的干扰管理模型,设计相应的求解算法,通过算例及应用实例验证模型及其算法的有效性,为物流企业解决顾客时间窗变化的问题提供理论方法支持。本文的主要研究工作如下：
(1)顾客时间窗变化的干扰管理问题分析
分析物流企业针对顾客时间窗变化事件的典型处理流程,将整个处理流程提炼为顾客与物流企业之间的一个多阶段的协商过程,进而针对这一过程中“顾客变更时间窗后如何调整行车路线,以使系统扰动最小化”、以及“一旦顾客变更后的时间窗对系统扰动较大,物流企业无法接受,则如何在新时间窗附近再次向顾客推荐一个可行的时间窗”这两个问题提出处理思路,为问题的建模与求解奠定基础。
(2)顾客时间窗变化的干扰管理模型研究
从顾客、车辆驾驶员、物流企业三个方面衡量顾客时间窗变化对物流配送系统产生的扰动程度,建立“顾客时间窗变化的车辆路线调整模型”、“新时间窗推荐模型”这两个干扰管理子模型,并进而形成多阶段的顾客时间窗变化的干扰管理模型。
(3)顾客时间窗变化的干扰管理模型的求解算法研究
针对“顾客时间窗变化的车辆路线调整模型”,以干扰事件发生时的问题状态为基础,采用二维染色体结构,以及适合多目标决策模型的适应值计算方法,设计出用于求解该类模型的一种遗传算法；同时提出该模型的启发式求解算法,该算法基于新车增派和多车协作的启发式知识,针对受扰车辆路线上的所有顾客,依次将每一顾客在原计划的配送车辆、其他在途车辆、以及增派的新车之间分配；针对“新时间窗推荐模型”,在受扰车辆的未服务顾客的排序结果中调整变更时间窗的顾客的前后顺序,并利用启发式救援算法计算系统扰动程度能够容忍的一个未服务顾客排序结果,进而对顾客服务时间进行松弛,得到可推荐的新时间窗。
(4)顾客时间窗变化的物流配送干扰管理应用研究
运用标准算例验证求解算法的有效性：并以京东物流在大连市的快件配送为应用背景,针对实际的顾客时间窗变化事件,运用本文的干扰管理模型及其求解算法进行问题处理,验证本文所提方法的有效性。
本研究针对顾客时间窗变化这一物流配送干扰管理难题进行了有益的探索,有利于提高物流配送干扰管理的科学性、有效性,改进目前物流企业所普遍存在的落后的干扰管理手段,增强物流配送干扰管理系统的鲁棒性,使物流配送干扰管理的理论与应用研究朝着物流企业与顾客相互协商的柔性化处理方向更进一步。
To meet customer needs as much as possible is an important means of improving the service quality and enhancing market competitiveness for every logistic enterprise. But in reality, customers may not be able to get to the given place at the given time as a result of accidental events. Therefore, they have to change their time windows of logistic services even if the delivery vehicles are running. It is a great challenge for logistic enterprises that the customer time window changes are not only inevitable but also hard to predict. Although all logistic enterprises wish to meet customers'demands, they have to consider their logistic capacities and system disruptions. They also have to answer customers based on the considerations of system disruptions and customers'satisfactions.
This paper focuses on the disruption management problem of customer time window changes. A disruption management model for customer time window changes is studied and its algorithm is designed, which are proved by application instances. This paper will present theory and methodology supports for logistic enterprises to solve the problems of customer time window changes. The main research work is summarized as follows:
(1) Problem analysis
A typical disruption management process of logistic enterprises is analyzed for customer time window changes in distribution. The process is concluded as a multi-stage discussion procedure between customers and logistic enterprises. Two problems that occurred in the procedure are discussed. The first problem is the changing of vehicle routes when customers have changed their time windows in order to minimize the system disruption. The second problem is to recommend a feasible time window which is nearest to the changed time window of a customer when the logistic enterprise can not accept the system disruption. The analysis of the disruption management problem forms the basis of modeling and solving the problem.
(2) Research on the disruption management model for customer time window changes
The disruption that the customer time window changes bring to the distribution system is measured by the three aspects of customers, vehicle drivers, and logistic enterprises. Two models are constructed:one is the vehicle route changing model after a customer time window changes, and the other is the new time window recommendation model. A multi-stage disruption management model for customer time window changes is made based on the above two models.
(3) Research on the disruption management algorithm for customer time window changes
A genetic algorithm is designed to solve the vehicle route changing model. In the algorithm, a two-dimension chromosome structure and a fitness function that fits multi-objective decision model are presented. A heuristic algorithm is also presented to solve the model. The heuristic iteratively assigns each disrupted customer to its old vehicle, other running vehicles, or an idle vehicle. To solve the new time window recommendation model, the sequence of the non-served customers of the disrupted vehicle is changed; and the best one will be chosen by the heuristic algorithm according to the system disruption. The customer time window of the best service sequence will be relaxed in order to obtain a recommended time window.
(4) Application research on the disruption management problem for customer time window changes in distribution
Benchmark instances are used to test the validities of the algorithms. The presented disruption management model and algorithm are also used to deal with the customer time window changes in the express and distribution of JingDong Logistics in order to test their validities.
This paper contributes to the exploration of the disruption management problem for customer time window changes in distribution. It helps to increase the instantaneity and the rationality of the disruption management in distribution and improve the old disruption management method in most logistic enterprises. The results should enhance the robustness of the disruption management system in distribution, and promote the theory and application research of disruption management towards the flexible interactions between logistic enterprises and customers.
引文
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