Research The 4PL Route Optimization Considering The Customer’s Time Of Psychological Preference

Logistics refers to the entity flow process which is from the supplying place to the receving place, to realize the transportation and distribution operation of raw materials, semi-finished products and finished products. Logistics is not only the product of current economic globalization, but also the important link of promoting economic globalization. With the increasingly strengthening and improving of the demand of the logistics service, the logistics service industry in the conventional sense is incapable of meeting needs, then the Fourth Party Logistics is emerged (The Four Party Logistics,4PL)The four party Logistics refers to the integrator of a supply chain. It has the ability to solve the route optimization problem and selection problem of the third party logistics provider (3PL) in the logistics network. In other words, this is the basic fourth party logistics route optimization problem. In the case of the basic fourth party logistics route optimization problem, this paper puts forward the fourth party logistics route optimization problem considering the customer’s time of psychological preference.At first, in order to describe the choice behavior about the customer’s time of psychological preference, we respectively base on the theory of expectation effect and the cumulative prospect theory to depict this behavior, and then establish three different kinds of mathematical models about the 4PL route optimization problem.Secondly, according to the characteristics of the problem model, we use the discrete domain ant colony algorithm to solve. We know there are two common discrete domain ant colony algorithm which is respectively ant colony system algorithm and maximum minimum ant colony system algorithm. We design hybrid ant colony algorithm which combines the characteristics of ant colony system algorithm and maximum minimum ant system algorithm.Then, we use the hybrid ant colony algorithm to solve the model which is based on the cumulative prospect theory. According to three different size instances, we analyze the algorithm parameters and acquire an algorithm parameter table of hybrid ant colony algorithm in three different instances. Then we compare the final route solution with enumeration algorithm, ant colony system algorithm and maximum minimum ant colony system algorithm respectively. And this proves that the hybrid ant colony algorithm has more superiority than other algorithms. The final route solution is more accurate and suitable for different instances.Finally, we analyze the 4PL route optimization problem considering the customer’s time of psychological preference in this paper. In the three different instances, we firstly analyze the problem parameters which impact on the final route solution. We acquire the influence rule of parameters on the final route solution. At the same time, we use three different kinds of models to solve three different instances and make these results contrast and analyze each other. The result of examples shows the mathematical model based on cumulative prospect theory can better reflect the actual decision-making.