The Research About The Path Optimization On The Fourth-Party Logistics

With the progress of science and technology, traditional logistics is to convert to more specialized modern logistics, resulting in a lot of third-party logistics companies which are providing specialized logistics services. We call these companies as 3PL. In order to focus more on core business, many enterprises will outsource their logistics services to 3PL companies.However, for many companies, they have variety products, decentralized area of origin and supply, and the destinations of customers distributed throughout the world. Moreover, the number of 3PL companies are continually increasing. And these 3PL companies are independent decentralized and fragmented. Accordingly, 3PL can’t meet the needs of enterprises. The fourth-party logistics(4PL) appeared under the situation. 4PL logistics is a new operational mode of logistic. It is of importance to do research on how to play 4PL supplier logistics functions, improve efficiency and quality, reduce costs and environmental pollution, and the integration of the entire supply chain to meet the social needs of all kinds of material goods.There are a lot of challenging problems in 4PL. And the fourth-party logistics routing problem(4PLRP) is a more critical and complex research in 4PL research. On the current theoretical research, the overseas study of 4PL is under development, and the domestic research has just started. Because the lack of a systematic study, there are many gaps in these research.Aim at less domestic research of 4PL, the paper’ research focus on the 4PLRP. This paper analyzes the Fourth path optimization, combined with the actual situation, we studied the single-to-point, single- objective 4PLRP, and time-based satisfaction single multipoint,multi-objective 4PLRP. Then according to the two research problem, we established the corresponding mathematical model.For the single-to-point, single-objective 4PLRP problem, in order to solve the disadvantage of low efficiency and bad optimal solution when using genetic algorithm to solve that 4PLRP problem, we propose an improved genetic algorithm based on heuristic local search strategy.This strategy can improve the local search performance of standard genetic algorithm. Finally,we test the improved algorithm’s performance on several different scale 4PL multigraphs.Experimental results show that the proposed algorithm can improve the convergence and stability of the algorithm. Moreover, compared to the standard genetic algorithm, the proposed algorithm can get better quality of optimal solution.For the time-based satisfaction single multipoint, multi-objective 4PLRP problem, we using fast non-dominated sorting genetic algorithm(NSGA-II) to solve it. Firstly, designing appropriate action operator according to the problem. For the defects of crowding calculationmethod in NSGA-II algorithm, we introduced a greedy elimination mechanism. At the same time,we introduced one self-adapting crossover and mutation probabilities. It can make the population has better diversity. Experiments are presented in the paper and simulation results and performance are analyzed. Experimental results show that the proposed algorithm can obtain better convergence and better Pareto optimal solution set.