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Mathematics
Special Issues
Optimization and Big Data in Logistics and Supply Chain Management
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Optimization and Big Data in Logistics and Supply Chain Management

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1018

Special Issue Editor

Prof. Dr. Alejandro Escudero-Santana

E-Mail Website
Guest Editor
Departamento de Organización Industrial y Gestión de Empresas II, Universidad de Sevilla, Avd. Camino de los Descrubrimientos s/n, 41092 Sevilla, Spain
Interests: optimization; machine learning; logistics; transport; production; supply chain

Special Issue Information

Dear Colleagues,

The application of Big Data in logistics and supply chain management is attracting growing attention because of the necessity of improving its efficiency. The new reality of computing, with greater storage and information processing capabilities, is allowing the development of new models and methods that focus on optimizing the different processes.

This Special Issue invites papers on both data analysis and optimization topics with potential application in logistics and supply chain management. We solicit papers that incorporate new ideas, results, innovative, modern methodologies and algorithms. We also encourage the submission of papers that include applications to real-life problems and its implementation results. 

Research papers, review articles and short communications are invited.

Dr. Alejandro Escudero-Santana
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization algorithms applied to logistic and supply chain management
  • heuristics and metaheuristics
  • mathematical model applied to logistic and supply chain management
  • best practices of big data models
  • new algorithms being developed specifically for big data environment
  • big data analytics
  • data science
  • analysis of real-time data in the optimization of logistics processes
  • real-world applications of artificial intelligence and machine learning in big data

Published Papers (1 paper)

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Research

13 pages, 279 KiB  
Article
A Decision-Focused Learning Framework for Vessel Selection Problem
by Xuecheng Tian, Yanxia Guan and Shuaian Wang
Mathematics 2023, 11(16), 3503; https://doi.org/10.3390/math11163503 - 14 Aug 2023
Viewed by 608
Abstract
Maritime transportation safety is pivotal in international trade, with port state control (PSC) inspections being crucial to vessel safety. However, port authorities need to identify substandard vessels effectively because of resource constraints and high costs. Therefore, we propose robust predictive models and optimization [...] Read more.
Maritime transportation safety is pivotal in international trade, with port state control (PSC) inspections being crucial to vessel safety. However, port authorities need to identify substandard vessels effectively because of resource constraints and high costs. Therefore, we propose robust predictive models and optimization strategies for vessel selection, using the random forest (RF) algorithm. We first use a traditional RF model serving as a benchmark, denoted as model M0. Then, we construct model M1 by refining the RF algorithm with a batch-processing method, thereby providing a better measure of the relative relationship between the predicted deficiency counts within a batch of ships. Then, we propose model M2, incorporating a decision-focused learning (DFL) framework into the tree construction process, enhancing the decision performance of the algorithm. In addition, we propose a variant model of M2, denoted as M2-0, considering the worst-case scenario when designing the decision loss function. By conducting experiments with data from the port of Hong Kong, we demonstrate that models M1 and M2 offer superior decision-making performance compared to model M0, and model M2 outperforms model M2-0 in both decision performance and stability. We further verify the robustness of these models by testing them under various instance scales. Overall, our study enhances the PSC inspection efficiency, ultimately bolstering maritime transportation safety. Full article
(This article belongs to the Special Issue Optimization and Big Data in Logistics and Supply Chain Management)
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