A Two-stage Algorithm for Production Distribution Optimization of Fresh Products
DOI:
https://doi.org/10.23055/ijietap.2025.32.1.10119Keywords:
Vehicle Routing Problem; Production Scheduling; Fresh Products; Ant Colony Optimization; Genetic Algorithm.Abstract
The rise of e-commerce and the just-in-time system has imposed more stringent demands on fresh product supply chains. This paper addresses the challenges of production and distribution decision-making under uncertainty, considering the vehicle routing problem with time windows (VRPTW). Fresh products are distributed immediately after production, with any remaining perishable products deteriorating before they can be transported. To address these issues, a mathematical model is proposed for optimizing the production and distribution of fresh products. The objective optimization model for production scheduling and VRPTW is classified as an NP-hard problem. To tackle and optimize this complex problem, a two-stage algorithm combining ant colony optimization (ACO) and a fuzzy adaptive genetic algorithm (FAGA) is proposed. The approach begins by determining the critical combination parameters of the algorithm. Subsequently, analysis of the model's results reveals that production and distribution costs decrease significantly when integrated decision-making is employed. Additionally, the vehicle setup cost introduces a turning point in the overall target cost. Finally, a numerical experiment on VRPTW is conducted, with the results demonstrating the effectiveness of the proposed two-stage algorithm.
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