How Adaptive Genetic Algorithms Revolutionize Freight Dynamic Pricing

Background

The freight industry is on a rapid growth trajectory; HuoLala's 2023 prospectus shows a 28.8% year‑over‑year revenue increase, highlighting intense market dynamics. Pricing volatility and negotiation often lead to low driver response rates, necessitating a system that can guide driver price increases while maintaining efficiency and stability.

Proposed Adaptive Genetic Algorithm Framework

We introduce a self‑adaptive genetic algorithm (GA) framework that evolves pricing strategies under cost constraints, enabling dynamic adjustments and stable high returns despite fluctuating market conditions.

Solution Comparison

Three commonly used strategies are summarized:

Expert‑driven evolution: relies on domain knowledge and extensive A/B testing; impractical for the massive combinatorial space of freight O2O platforms.

Precise algorithmic strategies: causal inference models, dynamic programming, integer programming; require high‑quality data and suffer from bias and long‑tail distribution issues.

Heuristic algorithms: simulated annealing, tabu search, evolutionary algorithms, ant‑colony, GA. Our previous work combined GA with causal inference (CI‑GA), showing superior performance for dynamic pricing challenges.

Genetic Algorithm Business Challenges

Key challenges include premature convergence to local optima, performance evaluation under noisy environments, and maintaining population diversity.

Multi‑Armed Bandit (MAB)

MAB is a classic decision‑making framework that balances exploration and exploitation. Integrating the Upper Confidence Bound (UCB) algorithm into the GA fitness function helps guide the search toward promising yet under‑explored solutions.

Adaptive Genetic Algorithm Improvements

We propose three enhancements:

Fitness function incorporates UCB to balance exploration and exploitation.

Statistical significance testing (t‑test, ANOVA) evaluates whether the population has stabilized, allowing dynamic adjustment of convergence speed.

Expert‑strategy diversity mechanism retains a set of expert‑proposed strategies with zero historical reward, ensuring they receive selection probability and preventing premature loss of potentially valuable policies.

Application to Driver Price Recommendation

Experiments compare three groups: manual expert strategy, causal inference with fixed price coefficient, and the adaptive GA with dynamic coefficient. Metrics include response rate, match rate, and cancellation rate. The adaptive GA achieved +0.77 pp response, +0.63 pp match, and –0.64 pp cancellation improvements.

Key observations: leveraging historical experiment results stabilizes gains; sample‑significance weighting prevents over‑reliance on noisy data; diversity mechanisms enable exploration of untested strategies, avoiding local optima.

Summary

By integrating UCB‑based fitness, statistical significance testing, and expert‑strategy diversity, the adaptive GA overcomes local‑optimum, noise, and diversity challenges, delivering consistent improvements in freight dynamic pricing.

Outlook

Future work will explore large‑language‑model (LLM) agents to embed expert knowledge directly into the pricing‑strategy loop, improving interpretability and accelerating iteration cycles.

References

Li Yan, Yuan Hongyu, Yu Jiaqiao et al., "Genetic Algorithms in Optimization Problems", Shandong Industrial Technology, 2019.

Zhou Jiaquan, "Improved Genetic Algorithm for Path Planning", Microcomputer Applications, 2021.

Performance Evaluation of Genetic Algorithms in Noisy Environments, Electronics Journal, 2010.

New Adaptive Genetic Algorithm Hybrid Reliability Optimization Model.

Genetic Algorithms in Dynamic Pricing.

HuoLala IPO Report, 2023 (Revenue growth 28.8%).

Dynamic Pricing Practice at HuoLala Peak.