Bibliographic Information: Yuan, Y., Xie, X., & Xie, Y. (Year). Supply Chain Optimization Strategies: An Empirical Study on Fresh Product Delivery Routes. [Journal Name Not Provided].
Research Objective: This paper investigates the logistical challenges of fresh product distribution for Y Chain Supermarket in Shenyang, Liaoning Province, China, and proposes an optimized delivery route plan using a genetic algorithm to minimize costs and improve efficiency.
Methodology: The study analyzes the existing delivery routes, vehicle loading conditions, and time window constraints of eight Y Chain Supermarket stores. A genetic algorithm is employed to model and optimize delivery routes, considering factors like transportation costs, penalty costs for time window violations, vehicle load capacity, and store demand.
Key Findings: Optimizing delivery routes with a genetic algorithm leads to a significant reduction in transportation costs (from 668 yuan to 174 yuan), eliminates penalty costs for late deliveries, increases vehicle loading efficiency (from 60% to 90% for Vehicle 1 and from 110% to 80% for Vehicle 2), reduces total transportation time by 17 minutes, and shortens the total distance traveled by 15 kilometers.
Main Conclusions: Implementing a genetic algorithm for route optimization significantly enhances the efficiency and cost-effectiveness of fresh product delivery for Y Chain Supermarket. The optimized routes ensure timely deliveries within specified time windows, reduce transportation costs, and improve vehicle utilization.
Significance: This research provides a practical application of genetic algorithms for optimizing logistics in the fresh food retail industry. The findings highlight the potential of such algorithms to address challenges related to transportation costs, delivery timeliness, and efficient resource allocation in fresh product supply chains.
Limitations and Future Research: The study focuses on a specific case study of Y Chain Supermarket in Shenyang and may not be generalizable to other contexts without further adaptation. Future research could explore the impact of factors like traffic conditions, weather variations, and demand fluctuations on the effectiveness of the proposed optimization model. Additionally, integrating real-time data and dynamic route adjustments could further enhance the efficiency of fresh product delivery systems.
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