Machine Learning for Planning and Logistics
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 9
Special Issue Editors
Interests: artificial intelligence; machine learning; operations research; constraint programming; satisfiability; optimization; forecasting
Special Issues, Collections and Topics in MDPI journals
Interests: scheduling; graph theory; optimization; mathematical modeling; supply chain optimization; logistics; transportation; production systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid growth of e-commerce, global trade, and just-in-time manufacturing, organisations face increasing pressure to improve delivery efficiency, reduce operational costs, and manage complex logistics networks. Logistics planning requires the management of goods, information or services, with many applications including supply chain optimisation, transport management, event planning, disaster planning, E-commerce. Objectives include cost, speed, profit and robustness under change. These problems have traditionally been modelled and solved using optimisation technologies such as mathematical programming, dynamic programming, and constraint programming, with forecasting methods used to predict demand, cost, and behaviour. However, machine learning is increasingly making inroads in these areas. In this Special Issue, we invite researchers to submit research applying machine learning to logistics planning applications.
Application areas include (but are not limited to) the following topics:
- Routing;
- Resource allocation;
- Warehouse management;
- Inventory management;
- Order management;
- Transportation;
- Production;
- Procurement;
Machine learning approaches of interest include (but are not limited To):
- Deep reinforcement learning;
- Supervised learning (regression, classification);
- Unsupervised learning (anomaly detection, pattern recognition);
- Bayesian methods;
- Predictive analytics;
- Generative AI (transformers, large language models);
- Multi-agent systems.
Dr. Steven Prestwich
Prof. Dr. Massimiliano Caramia
Guest Editors
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. Algorithms is an international peer-reviewed open access monthly 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 1800 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
- routing
- resource allocation
- warehouse management
- inventory management
- order management
- transportation
- production
- procurement
- deep reinforcement learning
- supervised learning (regression, classification)
- unsupervised learning (anomaly detection, pattern recognition)
- Bayesian methods
- predictive analytics
- generative AI (transformers, large language models)
- multi-agent systems
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