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Vehicle routing optimization and algorithms for instant delivery under customer loss mechanism
Gaoming Cao1,2, Weixiong Zha1
1School of Transportation Engineering, East China Jiaotong University, Nanchang, Jiangxi, China.
Plos One
|July 27, 2026
Summary
This study introduces a customer loss mechanism to optimize instant delivery services by identifying and excluding resource-intensive customers. This strategy effectively reduces delivery costs and improves resource utilization without compromising service quality.
Area of Science:
- Operations Research
- Logistics Management
- Applied Mathematics
Background:
- Instant delivery services face significant customer losses due to resource-demand mismatches.
- Optimizing delivery routes and resource allocation is crucial for profitability and efficiency.
Purpose of the Study:
- To develop a customer loss mechanism to screen resource-intensive customers.
- To establish a vehicle routing optimization model that minimizes costs including fixed, variable, and time window penalties.
- To evaluate the effectiveness of proactive customer exclusion in improving delivery efficiency.
Main Methods:
- Constructed an evaluation function to identify resource-intensive customers.
- Developed a vehicle routing optimization model incorporating a customer loss mechanism.
- Employed an improved genetic algorithm for model solving.
- Conducted case studies and large-scale experiments for validation.
Main Results:
- The improved genetic algorithm demonstrated superior performance over traditional methods, reducing total delivery costs by up to 36.25%.
- Proactively excluding 8.33% of customers reduced total delivery costs by 17.18% mainly through fleet size reduction.
- Excluding 10% of marginal customers led to an 18.39% total delivery cost reduction with zero violations.
Conclusions:
- The customer loss mechanism precisely screens inefficient delivery nodes, enhancing resource utilization.
- Proactive customer loss is a feasible and effective strategy for optimizing instant delivery operations.
- The model proves robust across varying resource constraints, highlighting its practical applicability.
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