Logistics-client stable matching under 4PL to reduce the empty-loaded rate

Area of Science:

• Operations Research
• Supply Chain Management
• Industrial Engineering

Background:

• The logistics industry, a key tertiary sector, faces significant resource wastage due to empty return trips.
• Current logistics patterns result in approximately 50% of delivery resources being underutilized during return journeys.
• Addressing the empty-loaded rate is crucial for economic efficiency and sustainability in logistics.

Purpose of the Study:

• To develop and validate a logistics-client matching model under a fourth-party logistics (4PL) framework.
• To minimize the empty-loaded rate of logistics vehicles.
• To enhance the fairness and efficiency of logistics resource allocation.

Main Methods:

• Construction of a preference calculation model between logistics providers and clients.
• Development of a linear one-to-one stable matching model for small-quantity clients, based on the stable marriage problem.
• Creation of a linear many-to-one stable matching model for large-quantity clients, incorporating a novel many-to-one stable matching constraint.
• Validation through case studies demonstrating large-scale and global optimization capabilities.

Main Results:

• The proposed stable matching model effectively reduces the empty-loaded rate of logistics vehicles.
• The model demonstrates superior performance compared to ordinary matching models in reducing empty trips.
• Case studies confirmed the model's capability for large-scale and global optimization.
• The matching solutions generated by the model were found to be fairer for all parties involved.

Conclusions:

• The developed 4PL stable matching model offers a viable solution to the persistent problem of empty-loaded return trips in logistics.
• The model enhances operational efficiency and resource optimization within the logistics sector.
• The approach provides a fairer and more equitable distribution of logistics services and resources.