Research On Agricultural Materials Transportation Management Based On The Potential Demand Of Farmers

Providing door-to-door services in agricultural materials delivery is an important competition means for agricultural materials enterprise to acquire customers, which is directly related to the agricultural materials sales and market share of enterprises. Due to the current situations in farmers in China, for example, dispersed geographically, less demand, different delivery time, it make "the last kilometer" vehicle distribution of agricultural materials account for the half of the shipping cost and delivery time. Therefore, how to arrange for agricultural materials vehicle delivery route directly affects the distribution cost and service level. At the same time, because of the demand uncertainty of the agricultural materials, the affect from the farmer group purchasing behavior, the comparison of neighbors, the crop planting area, and the influence of weather factors, the potential demand often occur in the process of agricultural materials distribution. If these potential agricultural materials requirements can be loaded in advance on the logistics distribution vehicles, the number of shipments for delivery vehicle and the distribution cost may reduce, improving the distribution efficiency of enterprises and increasing enterprise economic benefits. In addition, in the process of agricultural materials vehicle distribution, there are often various kinds of interference events:firstly, in the demand of agricultural materials, such as farmers increase or decrease the amount of agricultural materials demand, farmers cancel the original agricultural materials orders for some reason and the new customers emerges, etc.; secondly, in the delivery time and address, such as farmers change the receiving time by reason of temporary something and farmers change the delivery address because of transport need, etc.; in the delivery vehicles, such as vehicle fails, road blockage, etc.. How to generate new agricultural materials vehicle distribution route after these interference events occur is a very challenging problem.Based on the above analysis, this paper studies the agricultural materials vehicle scheduling and interference management considering the potential demand, which mainly includes the forecasting method of potential agricultural materials demand, the agricultural materials vehicle scheduling model and algorithm considering the potential demand, and the agricultural materials vehicle scheduling interference model based on the potential demand. The contributions of this paper and the possible innovation places are as follows:(1) Aiming at some difficulties in the process of agricultural materials distribution, such as the complex factors affecting the potential agricultural materials demand, different influence dimensions and the forecast indicators data is difficult to obtain, this study is based on overall principle, objective principle, the principles of practicability and operability, identifies16specific indicators affecting the potential agricultural materials demand from products, customers and enterprises three aspects. Then using the reduction rule of rough set (RS), the reduction method of potential agricultural materials demand forecasting index based on rough set is presented.6indicators is obtained based on the proposed reduction method and finally, the agricultural materials potential demand forecasting method based on RS-SVM (Rough Set Support Vector Machine) is constructed to effectively predict the potential demand in the process of agricultural materials distribution that provides the basis for the vehicle route optimization considering potential agricultural materials demand.(2) Considering the buying behavior influence between farmers each other in the process of the actual agricultural materials vehicle distribution, this study first describes the potential agricultural materials vehicle scheduling problem by considering the potential demand, then the model assumptions are given, and the agricultural materials vehicle route optimization model with soft time windows considering the potential demand is established. The CSGA algorithm (Constraint Satisfaction-Genetic Algorithm) is developed by optimizing the optimization mechanism of constraint satisfaction algorithm using genetic algorithm. Finally, Numerical examples verify the effectiveness of the proposed model and algorithm that make the agricultural materials enterprises load the potential demand in advance on the distribution vehicles such that it helps to decrease the number of shipments for delivery vehicles and the distribution cost, improve enterprise benefits.(3) For all kinds of possible interference events in the process of agricultural materials distribution, this study constructs the new farmers interference event recognition and measurement method, then quantifies the negative effects of the new farmers interference event on the original plan in farmers service time interference, agricultural materials distribution route interference and the cost of agricultural materials distributors three aspects, and presents the method converting other types of interference events into the new farmers interference event. In the following, the method to find the optimal departure time of the agricultural materials distribution vehicle is presented, based on which the agricultural materials vehicle scheduling interference recovery model is established and the genetic algorithm and nested segmentation algorithm is designed for solving the model. Finally, the numerical simulation illustrates the effectiveness of the proposed model and algorithm.(4) Taking an agricultural materials co., LTD in Yongnian handan city as the study object, Hankeyu1, Han682, Warsun Corn applied fertilizer and Silver cotton applied fertilizer four kinds of agricultural materials products are chosen as the actual research objects in this study according to the product type and weight. Firstly, the potential agricultural materials demand forecasting method based on RS-SVM is used to predict the potential demand of these four agricultural materials products during100times distribution process, and the method is proved to be effective by comparing the predicting results with the actual demands. Then, in terms of the farmer demand data in the distribution process and considering the potential demands, the50times distribution route in this case is optimized by adopting the agricultural materials vehicle route optimization model and algorithm; finally, by the interference event statistics in100times distribution process and choosing5times distribution process of Hankeyu1as an example, the real-time distribution route adjustment is shown, also the redundant loading strategy, differentiation strategy, late delivery vehicle and real-time adjustment strategy are proposed to reduce the enterprise distribution costs.The main contents of this paper are organized as following:Chapter1firstly introduces the research background and significance, research questions and objectives of this work, then the research thought, the technical route and the main research contents, research method and experimental means are also introduced briefly.Some related theoretical foundations are reviewed in detailed in Chapter2, for example, rough set, support vector machine and support vector machine regression, and genetic algorithm. Moreover, the review of the previous work on agricultural materials logistics distribution and finding the new customers, rough set and support vector machine, vehicle scheduling optimization and interference management are made comprehensively and systematically.In Chapter3, the initial index system of the agricultural materials potential demand prediction is constructed in agricultural materials itself, farmers and distribution enterprises three aspects; then in the light of the index system, the agricultural materials potential demand forecasting model based on RS-SVM is developed. According to the real-world data, the agricultural materials potential demand forecasting index system after reduction by rough set is obtained and the effectiveness of the agricultural materials potential demand forecasting method based on RS-SVM is illustrated by simulating the collected real-world data.Chapter4considers the buying behavior influence between farmers each other in the process of the actual agricultural materials vehicle distribution, establishes the agricultural materials vehicle route optimization model considering the potential demand. Based on standard genetic algorithm and constraint satisfaction technique, a novel CSGA algorithm is developed to solve the multi-constrains mathematical model and finally, simulation results show that the proposed model and algorithm is effective.In Chapter5, by aiming at all kinds of possible interference events in the process of agricultural materials distribution, this study quantifies the negative effects of the new farmers interference event on the original plan in farmers service time interference, agricultural materials distribution route interference and the cost of agricultural materials distributors three aspects, and presents the method converting other types of interference events into the new farmers interference event. In the following, the method to find the optimal departure time of the agricultural materials distribution vehicle is given, also the agricultural materials vehicle scheduling interference recovery model is then established; and the Genetic algorithm and nested segmentation algorithm is designed for solving the model. Finally, the numerical simulation illustrates the effectiveness of the proposed model and algorithm.Chapter6takes an agricultural materials co., LTD in Yongnian handan city as the study object, Hankeyu1, Han682, Warsun Corn applied fertilizer and Silver cotton applied fertilizer four kinds of agricultural materials products are chosen as the actual research objects in this study according to the product type and weight. The case study is finished based on the proposed methods and algorithms which provide the concrete the distribution strategy for enterprises.The conclusions are remarked in Chapter7.