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Green Order Sorting Problem in Cold Storage Solved by Genetic Algorithm
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DOI：10.3390/su16209062.png)
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Abstract
En 中文
This study investigates the efficiency of cold storage warehouses and contributes to sustainable supply chain management by integrating eco-friendly practices into storage operations. In facilities for milk and its derivatives, unregulated order handling significantly increases energy consumption due to frequent door openings in the cooler. To address this challenge, we developed a novel mathematical model aimed at optimizing order sequences and minimizing energy costs, addressing a previously unexplored gap in the literature. A genetic algorithm (GA) was employed to solve this model, with careful consideration of carbon emissions generated during the algorithm's execution. We utilized the Yates notation, an experimental design technique, to systematically optimize the GA's parameters, ensuring robust and statistically valid results. This methodology enabled a thorough analysis of the factors influencing energy consumption. The findings enhance energy efficiency in cold storage warehouses, leading to reduced carbon dioxide emissions and fostering sustainable practices within supply chain management. Ultimately, this study successfully integrates green practices into cold storage operations, supporting broader sustainability objectives.
Keywords:
cold storage
green supply chain
genetic algorithm
green order picking
carbon footprint
Journal
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3.3
Papers:
10.7W
Citations:
28.4W
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Cited Papers
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SUSTAINABILITY
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SUSTAINABILITYIF3.3
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