JOURNAL ARTICLE
Multi-Objective Last-Mile Vehicle Routing Problem for Fresh Food E-Commerce: A Sustainable Perspective.
Published In: International Journal of Information Technology & Decision Making, 2024, v. 23, n. 6. P. 2335 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Liu, Xing; Gou, Xunjie; Xu, Zeshui 3 of 3
Abstract
Last-mile logistics poses a pivotal challenge for the sustainable prosperity of fresh food e-commerce. However, there has been insufficient research attention towards optimizing the last-mile vehicle routing from three sustainable dimensions simultaneously based on triple-bottom-line. To narrow this gap, this paper proposes a multi-objective last-mile vehicle routing model for fresh food e-commerce, which takes the three sustainable dimensions as independent objectives. The economic and environment objectives are minimizing the total cost and carbon emission, respectively. For social dimension, a nonlinear function is proposed based on the Prospect theory, which quantifies customers' satisfaction when they perceive delivery time. Then the social objective is described as maximizing customers' satisfaction. Subsequently, the model is solved by the nondominated sorting genetic algorithm II and validated by a numerical experiment. Based on the experiment results, this paper recommends that logistics enterprises should prioritize the trade-off among the three sustainable dimensions. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Information Technology & Decision Making. 2024/11, Vol. 23, Issue 6, p2335
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2024
- ISSN:0219-6220
- DOI:10.1142/S0219622024500020
- Accession Number:181623525
- Copyright Statement:Copyright of International Journal of Information Technology & Decision Making is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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