Automobile factory Oukai standard electric truck - an essential tool for efficient logistics

Route optimization and real-time scheduling are a research hotspot in the current field. The main methods used in practice are:
. Mathematical programming method
Selecting the best task and the best path can be summarized as a task scheduling problem. Mathematical programming method is a traditional method for solving optimal solutions to scheduling problems. The solution process of this method is actually an optimization process under resource constraints. Practical methods mainly include integer programming, dynamic programming, r method, etc. This type of method can obtain better results in small-scale scheduling situations. However, as the scheduling scale increases, the time it takes to solve the problem increases exponentially, which limits the application of this method in responsible, large-scale real-time route optimization and scheduling.
. Simulation method
The simulation method conducts computer simulation of the implementation of a scheduling plan by modeling the actual scheduling environment. Users and researchers can use simulation methods to test, compare, and monitor certain scheduling solutions to change and select scheduling strategies. The methods used in practice include discrete event simulation method, object-oriented simulation method and dimensional simulation technology. There are many software that can be used for scheduling simulation. Among them, the group's software can quickly establish simulation models to achieve three-dimensional demonstration of the simulation process and analysis and processing of results.
. Artificial Intelligence Method
The artificial intelligence method describes the scheduling process as a process of searching for the optimal solution in a solution set that satisfies constraints. It uses knowledge representation technology to include human knowledge and uses various search technologies to strive to give a satisfactory solution. Specific methods include expert system methods, genetic algorithms, heuristic algorithms, and neural network algorithms. Among them, the expert system method is mostly used in practice. It abstracts the experience of scheduling experts into scheduling rules that the system can understand and execute, and uses conflict resolution technology to solve the problem of rule expansion and conflicts in large-scale scheduling.
Because neural networks have the advantages of parallel operations, distributed knowledge storage, and strong adaptability, they have become a promising method for solving large-scale scheduling problems. At present, the neural network method has been successfully used to solve the problem - in problem solving, the neural network can convert the solution of the combinatorial optimization problem into the energy function of a discrete dynamic system and obtain the solution of the optimization problem by minimizing the energy function.
Genetic algorithm is an optimization solution method formed by simulating inheritance and mutation in the process of biological evolution in nature. When solving the optimal scheduling problem, the genetic algorithm first expresses a certain number of possible scheduling solutions into appropriate chromosomes through coding and calculates the fitness of each chromosome (such as the shortest running path). Through repeated replication, crossover, and mutation, it searches for the largest fitness The chromosome is the optimal solution to the scheduling problem.
Using one method alone to solve scheduling problems often has certain flaws. At present, the scheduling problem that is solved by integrating multiple methods is a research hotspot. For example, the integration of expert systems and genetic algorithms integrates expert knowledge into the formation of the initial chromosome group to speed up the solution speed and quality.
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