Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/41324
Title: | Performance Evaluation and Explainability of Last-Mile Delivery |
Author: | Brochado, Ângela Filipa Rocha, Eugénio Addo, Emmanuel Silva, Samuel |
Keywords: | Last-mile logistics Performance evaluation and improvement recommendations Multi-directional efficiency analysis (MEA) eXplainable Artificial Intelligence (XAI) |
Issue Date: | 2024 |
Publisher: | Elsevier |
Abstract: | The demand for last-mile delivery (LMD) services worldwide increased following online sales growth, so better methods to assess efficiency issues are paramount. This work explores a data-driven approach to evaluate LMD services and inform logistics service providers about possible improvement directions. It uses multi-directional efficiency analysis to benchmark LMD services based on process variables, such as delivery time and service cost. Then, by fitting machine learning models and using explainability algorithms with new metrics, characterizes factors that influence LMD performance. Early discussions with experts show that the approach produces understandable and integrable results that generate valuable insights, e.g., regarding the impact of each variable on service quality informing the direction for further improvement action. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/41324 |
DOI: | 10.1016/j.procs.2024.02.067 |
ISSN: | 1877-0509 |
Appears in Collections: | CIDMA - Artigos DEGEIT - Artigos DETI - Artigos DMat - Artigos IEETA - Artigos FAAG - Artigos |
Files in This Item:
File | Description | Size | Format | |
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1-s2.0-S1877050924002448-main.pdf | 1.12 MB | Adobe PDF | View/Open |
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