Research on vehicle scheduling based on C-W algorithm

Vehicle scheduling is one of the most important links in logistics distribution, a large number of demands also lead to logistics companies have to reasonably arrange the transportation of vehicle routes to save costs. By optimizing the distribution routes of vehicles, logistics companies can save transportation mileage and thus save transportation costs. In this paper,10 demand points of an agricultural products logistics company in Beijing are selected, and the location of the distribution center is obtained through the center of gravity method. Finally, the route allocation is completed by using the mileage saving algorithm, and the scheduling of transport vehicles is realized. The example shows that the mileage saving algorithm can optimize the vehicle route and realize route allocation.

Research on Logistics Distribution Vehicle Scheduling Based on Heuristic Genetic Algorithm

To study the genetic algorithm, this paper solves the problem of shop scheduling under the premise of layout Flying − V . Firstly, double-layer coding is used for optimization. When calculating fitness, the time to return to the mouth P & D approaches the optimum through the greedy idea. Individual screening is carried out through the roulette method. Different crossover and genetic operators are used for different coding layers. Through thinking of elitism and catastrophe and the immigration operator to ensure the diversity of the algorithm in the calculation process, it can achieve the recommendation of the number of cars to control the cost. The stability of the algorithm is good. It can recommend a better picking sequence and number of carts for various types of picking problems.

A Customer Clustering Algorithm for Power Logistics Distribution Network Structure and Distribution Volume Constraints

Customer clustering technology for distribution process is widely used in location selection, distribution route optimization and vehicle scheduling optimization of power logistics distribution center. Aiming at the problem of customer clustering with unknown distribution center location, this paper proposes a clustering algorithm considering distribution network structure and distribution volume constraint, which makes up for the defect that the classical Euclidean distance does not consider the distribution road information. This paper proposes a logistics distribution customer clustering algorithm, which improves CLARANS algorithm to make the clustering results meet the constraints of customer distribution volume. By using the single vehicle load rate, the sufficient conditions for logistics distribution customer clustering to be solvable under the condition of considering the ubiquitous and constraints are given, which effectively solves the problem of logistics distribution customer clustering with sum constraints. The results state clearly that the clustering algorithm can effectively deal with large-scale spatial data sets, and the clustering process is not affected by isolated customers, The clustering results can be effectively applied to the distribution center location, distribution cost optimization, distribution route optimization and distribution area division of vehicle scheduling optimization.