A Multi-objective Optimization Models And Algorithms Of Integrated Logistics System With Time Competition Environment

With the development of global economic integration, the variety and diversity of our requirements present higher demands for logistic service, which makes the enterprises be in a fierce time-based competitive market environment. Meanwhile building of a resource conserving and environmentally friendly society attracts people’s attention to reverse logistics. Driven by environment-related rules and financial interests, reverse logistics has been an important issue focused by enterprise managers and researchers in related field. Time-based competitive environment asks for many new requirements for enterprises. In order to realize customer service goal, logistic system has to adapt to this new environment, and the birth of integrated logistics management provides a good basis for solving this problem. However, most of the studies on integrated logistics are focused on single objective from a certain perspective. So, this paper will consider customer’s satisfaction on time under time-based competitive environment and the optimization of general logistics total cost, by introducing reverse logistics to further conduct the study considering the three aspects of objective optimization at the same time.The first part of this paper aims to introduce the research background, research objectives, research significance, the current research situation at home and abroad, the main contents and innovations.The second part of this paper mainly deals with the issues of logistics management in time-based competitive environment, customer satisfaction and multi-objective optimization and customers’satisfaction on time, and so on.The third part of this paper takes supply chain secondary distribution network as the subject, based on customers’ demands on deliver time, and designs a time satisfaction function, according to which, the paper build a double goal programming model. One goal is to minimize the total cost of the system; the other goal is to maximize customer satisfaction on time. And then the paper solves the model by using genetic algorithm and does a calculation on the model with examples.The fourth part of this paper introduces the reverse logistics based on the third part, and makes some improvements on genetic algorithm by taking advantage of simulated annealing so as to solve the examples in LRP standard database of University of Aveiro to verify the validity of the model and algorithm.Finally in the fifth part of this paper, the author makes a conclusion and looks forward, and points out the direction to improve in the future.The results of the research also have a positive effect to improve the global competitiveness of our enterprises and give references to solving the construction and operation of logistical material support system for rescue and relief work.