Location optimization of fresh food e-commerce front warehouse

Area of Science:

• Operations Research
• Supply Chain Management
• E-commerce Logistics

Background:

• The fresh produce e-commerce sector has rapidly expanded, influenced by global events like COVID-19 and advancements in cold chain logistics.
• Understanding consumer demand for fresh products is crucial for optimizing e-commerce operations.

Purpose of the Study:

• To develop a location allocation model for fresh produce e-commerce front warehouses aimed at minimizing total operational costs.
• To introduce and validate an improved immune optimization algorithm for solving the location allocation problem.

Main Methods:

• Construction of a location allocation model for front warehouses in fresh e-commerce.
• Development and application of an improved immune optimization algorithm.
• Validation through a real-world case study comparing with traditional genetic algorithms.

Main Results:

• The improved immune optimization algorithm demonstrated superior solution accuracy compared to the traditional genetic algorithm.
• The proposed location model effectively supports fresh produce e-commerce enterprises in opening new front-end warehouses.
• The model provides optimal economic decision-making for front warehouse layout strategies.

Conclusions:

• The developed location allocation model and improved immune optimization algorithm offer a robust solution for the challenges in fresh produce e-commerce logistics.
• This research provides valuable insights for businesses seeking to optimize their front warehouse network and respond to increasing demand.