Energy enterprise bulk commodity logistics transportation path optimization model

Abstract

The invention relates to an energy enterprise bulk commodity logistics transportation path optimization model, and provides a bulk commodity logistics transportation path optimization model and an application method thereof, and the model can dynamically adjust the transportation path to adapt to market changes, improve the flexibility and response speed of a supply chain, reduce the logistics cost and improve the transportation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of logistics transportation, especially the optimization technology of logistics transportation routes for bulk commodities such as coal and electricity. Specifically, the present invention aims to optimize the logistics transportation routes of bulk commodities by constructing a dynamic transportation optimization model, so as to reduce logistics costs, improve transportation efficiency, and enhance the flexibility and responsiveness of the supply chain. Technical Background

[0002] Under the background of current globalization and regional economic integration, the selection and optimization of logistics transportation routes have become a key link in supply chain management. Especially in the field of bulk commodities, such as the coal and electricity industries, effective logistics strategies play a crucial role in reducing transportation costs, improving operational efficiency, and ensuring the stability of the supply chain. However, existing logistics transportation route optimization methods often lack the ability to dynamically adjust and cannot adapt to market changes and improve the flexibility and response speed of the supply chain. Summary of the Invention

[0003] The purpose of the present invention is to provide an optimization model and its application method for the logistics transportation routes of bulk commodities. This model can dynamically adjust transportation routes to adapt to market changes, improve the flexibility and response speed of the supply chain, reduce logistics costs and enhance transportation efficiency.

[0004] Technical Solutions

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] (1) Model construction: An integrated production, transportation, sales, and consumption monthly dynamic transportation optimization model: This model constructs a comprehensive analysis framework based on network flow theory, transportation economics theory, supply chain management theory, optimization theory, and game theory. The model realizes the optimization of logistics transportation routes through the definition of decision variables, the establishment of objective functions, the dimensionless processing of data, and the identification of constraint conditions.

[0007] (2) Optimization algorithm: The model adopts heuristic and meta-heuristic algorithm theories to solve the model using algorithms and obtain the optimized routes.

[0008] (3) Empirical research and optimization results: Through empirical research, the model analyzes the impact of the optimized transportation routes on costs and efficiency, and evaluates its feasibility in actual operation. The optimized transportation routes can achieve a reduction of about 15% in logistics costs and a 20% increase in transportation efficiency. Specific Embodiments

[0009] The following describes the specific embodiments of the present invention:

[0010] (1) Model construction process:

[0011] Definition of decision variables: The key variables in the model include input variables and decision variables. Input variables cover the average monthly unit sales price and cost of each coal type in each coal mine, the average monthly unit production cost of each coal type in each coal mine, the transportation distance / transportation time and freight rate of each route, the coal demand of self-built power plants, etc. Decision variables include the transportation volume of coal types from coal mines to sales regions, the coal transportation volume from coal mines to self-built power plants, the transportation time and transportation cost of coal types from coal mines to sales regions, etc.

[0012] Establishment of the objective function: The objective function of the model is mainly to minimize the overall transportation cost while taking into account time efficiency and other operational constraints. The objective function can be set as a multi-objective optimization problem, and the weighted sum method is used to convert multiple objectives into a single objective.

[0013] Data dimensionless: To facilitate comparison and analysis, eliminate the differences between sample factors, unify the dimension, and simplify the data. In this study, the dimensionless processing of logistics transportation data was carried out, and the maximum-minimum normalization method was adopted to compress the data into the range of 0 to 1.

[0014] Identification of constraint conditions: The constraint conditions of the model include coal mine supply capacity constraints, sales region demand constraints, railway transport capacity constraints, port throughput constraints, and power plant / port inventory balance constraints, etc.

[0015] Python code implementation:

[0016] The following is a simplified Python code example for implementing the dimensionless processing of data:

[0017] python

[0018] import numpy as np

[0019] # Assume data is an array containing logistics transportation data

[0020] data = np.array([...])

[0021] # Calculate the minimum and maximum values

[0022] min_val = np.min(data)

[0023] max_val = np.max(data)

[0024] # Apply the maximum-minimum normalization

[0025] normalized_data = (data - min_val) / (max_val - min_val)

[0026] This method ensures that transport indicators of different dimensions can be compared fairly and provides a unified data basis for the optimization model.

[0027] (2) Selection and application of optimization algorithms:

[0028] In the present invention, a suitable optimization algorithm, such as genetic algorithm, ant colony algorithm, etc., is selected according to the characteristics of the problem, and the model is solved. These algorithms can effectively deal with the NP-hard characteristics of the logistics transportation path optimization problem and find the approximate optimal solution to the problem.

[0029] (3) Empirical research and optimization results:

[0030] By solving the optimization model, we obtained the optimized transportation path. Compared with the original path, the optimized path has a full-week railway freight time of 3.86 days in terms of multi-pulling and express transportation, which is 0.74 days lower than the same period last year, an increase of 4.77 million tons, and an increase in marginal benefits of about 110 million yuan. Through the refined organization of planning, ship-cargo matching, hauling and unloading, the average ship stay in the port in August was 76.7 hours, 34.7 hours (-31.2%) lower than the average level of the same period in the past three years. From June to August, it was reduced by 53 hours (-40.9%) continuously, increasing the marginal contribution by about 11 million yuan, and saving about 16 million yuan in ship demurrage for the self-owned power plant. In terms of efficient turnover, the average storage period of port cargo at Huanghua Port, Tianjin Terminal, and Zhuhai Terminal was 3.9, 4.7, and 12.6 days, respectively, down 5.8%, 1.5%, and 5.5% from the average level of the past three years. China Energy Group was able to achieve approximately 15% reduction in logistics costs and 20% improvement in transportation efficiency.

[0031] When the above technical solutions are implemented, the optimization model can significantly reduce logistics costs and improve transportation efficiency. At the same time, it has a high degree of flexibility and responsiveness, can adapt to market changes and improve the stability of the supply chain.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate and not to limit the technical solutions described in the present invention; therefore, although this specification has described the present invention in detail with reference to the above embodiments, ordinary technicians in this field should understand that the present invention can still be modified or equivalently replaced; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A bulk commodity logistics transportation route optimization model for energy enterprises, characterized in that: The model includes the definition of decision variables, the establishment of objective functions, dimensionless data processing and the identification of constraint conditions. The model according to claim 1, characterized in that The objective function is to minimize the overall transportation cost while taking into account time efficiency and other operational constraints. The model according to claim 1, characterized in that The data dimensionless processing adopts the maximum and minimum normalization method. The model according to claim 1, characterized in that The constraints include coal mine supply capacity, sales area demand, railway transportation capacity, port throughput and power plant / port inventory balance. A method for optimizing the transportation path of bulk commodity logistics using the above model is characterized in that: Solve the model through algorithms to obtain the optimization path.