A logistics network optimization method based on hybrid automata

Abstract

The invention relates to a logistics network optimization method based on a hybrid automata. The method comprises the following steps: 1) constructing a hybrid automata model of each device in the logistics network of an iron and steel enterprise; 2) the hybrid automata model of each device is extended to the whole process of steel production logistics optimization scheduling, and the hybrid automata model of steel enterprise logistics network scheduling system is constructed; 3) Optimize the hybrid automata model of the logistics network scheduling system of the iron and steel enterprise by using the graph search two-layer optimization algorithm; 4) the optimized model is used to optimize the scheduling of the steel enterprise logistics network, and the optimized scheduling scheme is obtained. Compared with the prior art, the invention scientifically and reasonably solves the problems of coordination and optimization in the optimization of the logistics network, which is conducive torealizing the maximization of the production profit and the comprehensive benefit of the production characteristic performance.

Description

technical field [0001] The invention relates to the technical field of network optimization of raw material logistics enterprises of iron and steel enterprises, in particular to a logistics network optimization method based on a hybrid automaton. Background technique [0002] Raw materials for iron and steel enterprises mainly include iron ore powder, lump ore, and clean coal. Raw materials are mainly transported to the factory area by railway transportation, water transportation, etc., and belt transportation is mainly used among the main processes and storage facilities in the enterprise. The logistics process of raw materials among the production units of an iron and steel enterprise is as follows: figure 1 shown. Bulk raw materials such as iron ore powder, lump ore, and clean coal are affected by factors such as market supply fluctuations and transportation time fluctuations, which lead to obvious imbalances in the arrival of these raw materials in iron and steel enter...

AI Technical Summary

Problems solved by technology

[0003] The existence of bilinear item constraints and a large number of discrete variables makes the logistics problem a non-convex Mixed-Integer Nonlinear Programming (MINLP) problem. Due to its non-convex characteristics, the MINLP model is a class of difficult complex problems to solve

At the same time, due to the complexity of the logistics network optimization problem of iron and steel enterprises, it brings certain complexity to the modeling, and cannot solve the coordination and optimization problems in the logistics network optimization, which limits the comprehensive benefit maximization of production profits and production characteristics.

Images