| Title |
A customizable optimization model for green e-commerce packing considering multiple orders and diverse box types |
| ID_Doc |
75031 |
| Authors |
Liang, KB; Yang, JL; Shan, M; Kong, LJ; Liu, HW |
| Title |
A customizable optimization model for green e-commerce packing considering multiple orders and diverse box types |
| Year |
2024 |
| Published |
|
| DOI |
10.1016/j.jclepro.2024.141249 |
| Abstract |
In response to the growing emphasis on green packing, e -commerce businesses actively pursue sustainable solutions that balance cost reduction with environmental conservation. Traditional research on three-dimensional bin packing has concentrated on maximizing spatial utilization under fixed box dimensions. However, in the practical context of e -commerce, packing boxes exhibit a wide range of sizes within a single warehouse. Customizing packing to prevent material and space wastage has become crucial for sustainable development. This paper innovates traditional three-dimensional packing research by introducing open dimensions instead of fixed ones. It addresses challenges in e -commerce packing, such as multiple orders and diverse bin shapes, to formulate a mixed -integer programming model. Focused on the carbon emission lifecycle's usage phase for packing boxes, the model aims to minimize associated costs while ensuring that items' weight, the center of gravity, sufficient support, and non -overlapping placement are maintained during the packing process. It also incorporates one-dimensional and two-dimensional fixed bin sizes. To solve this model, the paper proposes an improved hybrid genetic algorithm for stack sequence optimization. This algorithm systematically optimizes the dimensions for each box type. Building upon this, the study introduces an algorithm capable of estimating packing material consumption and the percentage reduction in carbon emissions during the packing process, given the known variability in packing box utilization. These two algorithms are integrated into an open -dimension packing box warehouse management ecological closed loop system designed for the B2B e -commerce context. In experiments with the proposed model and algorithms, the results demonstrate an average packing rate improvement of 10%-12% compared to alternative methods. Additionally, packing material consumption and carbon emissions are reduced by approximately 16.56%-19.85%, confirming the effectiveness of the proposed approach. |
| Author Keywords |
Green packing; Sustainable solutions; Multiple-bin types; Stacked clustering; Hybrid algorithm |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001200138500001 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology |
| PDF |
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