Cost-based multi-parameter logistics routing path optimization algorithm

Area of Science:

• Operations Research
• Logistics Management
• Transportation Engineering

Background:

• Traditional vehicle path optimization focuses on shortest distance, often neglecting real-world factors that increase logistics costs.
• Pursuing only the shortest path can lead to negative consequences, diminishing overall efficiency and increasing operational expenses.

Purpose of the Study:

• To develop a more realistic path optimization model for logistics distribution.
• To incorporate fuel consumption, cost, road gradient, and vehicle condition into pathfinding algorithms.
• To validate the effectiveness of the proposed model in reducing logistics costs.

Main Methods:

• Designed a path optimization model using a simulated annealing algorithm.
• Integrated vehicle load capacity and road conditions into the optimization model.
• Verified the algorithm's performance through a simulation case study with multiple distribution points.

Main Results:

• The path optimization strategy incorporating road gradient significantly reduced vehicle path costs.
• Considering vehicle load and road gradient factors proved effective in logistics transportation.
• The simulation demonstrated the practical applicability and cost-saving benefits of the proposed model.

Conclusions:

• Realistic path optimization must account for multiple variables beyond just distance, including fuel consumption and road characteristics.
• The simulated annealing algorithm effectively optimizes logistics routes by considering road gradient and vehicle load.
• Implementing this comprehensive approach leads to reduced transportation costs and improved logistics efficiency.