Model And Solution Method Of Disruption Management For The Change Of Location In Distribution

In distribution, the location of the customer will change for some reasons, which may disable the original operational plan being executed and make the distribution abnormal. After the change of location, how to revise the original operational plan and obtain a new one that minimizes the negative impact of disruptions, are the key problem of disruption management. Therefore, how to measure the deviation cost, is the central concern of disruption management in distribution. Based on the quantitative analysis, utilizing models and algorithms to obtain the revising plan is one of the important strategies for improving the science of decision making. However, due to the complexity of the distribution system, and the real-time and multi-objective characteristics of disruption management, it is hard for existing theoretical methods to obtain the plan that minimizes the negative impact of disruptions in consideration of the benefit of all. This research aims to improve the science of the decision making of disruption management in distribution. It studies the method to measure the deviation cost based on human behavior, the model of disruption management for the change of location in distribution, and the method to solve the model of disruption management. This research includes the following aspects.(1) The method to measure the deviation cost based on human behavior is studied. By combining with Prospect Theory and Fuzzy Theory, The change of location in distribution is analyzed. The value functions of the different objectives are constructed and normalized respectively. The dissatisfactory membership functions of the different objectives are obtained by Fuzzy Theory. In consideration of human behavior, the functions to measure the behavior perception are constructed and the method to measure the deviation cost based on human behavior is formed.(2) The model of disruption management for the change of location in distribution is studied. Based on human behavior, the typical attitudes of the customers are induced so that the customers could be segmented. The characteristics affecting the typical attitudes of the customers are analyzed and the typical basic attributes and transaction characteristics of different clusters are summarized. The problem of disruption management for the change of location in distribution is divided into multiple stages according to the characteristics of different clusters. The model of disruption management characteristics of multi-stage and multi-objective based on human behavior is formed by constructing the submodel at each stage, which contributes to obtain the plan that minimizes the negative impact of disruptions.(3) The method to solve the model of disruption management is studied. Since the existing method is difficult to optimize all the objectives simultaneously and the sum of vectors has the characteristic of taking all the vectors into consideration, the multi-objective optimization method based on the space vector is presented. The Hybrid Ant Colony Optimization (HACO) is designed to solve the model of disruption management which is a NP-hard problem. By conbining the multi-objective optimization method and the HACO, The method to solve the model of disruption management is proposed. The framework and solution steps are demonstrated. The program is developed by using the tools of "Jbuilder 9", "Access", etc. Finally, the effectiveness of our method is validated by providing a real-world case study.The research is an intersection of the theories of Operations Research, Behavioral Science and Fuzzy Theory, which is not only a beneficial exploration to obtain the plan that minimizes the negative impact of disruptions, but also helpful to contribute to the theory and method of disruption management. The research results combining with the techniques of collecting vehicle’s data and monitoring vehicles in real time can provide the decision support for real-time optimizing and scheduling of the distribution process. The research is significant in the sense of improving the service quality of logistics enterprises.