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Algorithms
Special Issues
Machine Learning for Planning and Logistics
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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 August 2026 | Viewed by 1702

Editors

Dr. Steven Prestwich

E-Mail Website
Guest Editor
School of Computer Science and Information Technology, University College Cork, T12 K8AF Cork, Ireland
Interests: artificial intelligence; machine learning; operations research; constraint programming; satisfiability; optimization; forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Massimiliano Caramia

E-Mail Website
Guest Editor
Department of Enterprise Engineering, University of Rome "Tor Vergata", 00133 Rome, Italy
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 1774 KB  
Article
Block-Wise State Encoding for Action-Masked Reinforcement Learning in Flexible Job-Shop Scheduling
by Kostiantyn Hrishchenko and Oleksii Pysarchuk
Algorithms 2026, 19(6), 423; https://doi.org/10.3390/a19060423 - 23 May 2026
Viewed by 517
Abstract
This paper addresses the flexible job-shop scheduling problem (FJSP) as a constrained combinatorial optimization task with a large discrete action space. Although action-masked reinforcement learning has shown promise for such problems, the effect of structured vector-state encoding in scheduling has received less attention. [...] Read more.
This paper addresses the flexible job-shop scheduling problem (FJSP) as a constrained combinatorial optimization task with a large discrete action space. Although action-masked reinforcement learning has shown promise for such problems, the effect of structured vector-state encoding in scheduling has received less attention. The main contribution of this work is a structured block-wise state representation and a multi-branch feature extraction module for action-masked Proximal Policy Optimization (PPO). The proposed representation decomposes the scheduling state into three heterogeneous components capturing resource availability, operation readiness, and temporal attributes of operation–machine alternatives. Instead of flattening these signals into a single vector, the proposed encoder processes each block separately before aggregation, with the aim of preserving semantic structure during policy learning. To isolate the effect of representation design, we compare the proposed multi-branch encoder with a baseline single-branch multilayer perceptron under identical PPO hyperparameters and training conditions. Experiments on the Brandimarte MK benchmark suite show that the proposed architecture yields a lower best-achieved makespan on nine of ten instances and improves the best baseline result by up to 27.84%. Additional validation on selected Behnke and Geiger instances indicates that the BR encoder’s advantage extends to larger FJSP cases while preserving sub-second inference. Full article
(This article belongs to the Special Issue Machine Learning for Planning and Logistics)
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18 pages, 1340 KB  
Article
Spatiotemporal Optimization of Oilfield Electricity Consumption: A Multi-Objective Modeling Approach with Machine Learning
by Wenrong Song, Yuan Xu, Bin Lyu, Wenbin Liu, Yuxuan Zhang and Jin Wang
Algorithms 2026, 19(5), 401; https://doi.org/10.3390/a19050401 - 17 May 2026
Viewed by 384
Abstract
Oil enterprises face the challenge of reconciling escalating energy conservation targets with persistent production requirements, necessitating sophisticated electricity management solutions. The conventional ton-per-kWh allocation approach, often manually adjusted based on historical production and planning data, lacks a scientific basis and fails to accurately [...] Read more.
Oil enterprises face the challenge of reconciling escalating energy conservation targets with persistent production requirements, necessitating sophisticated electricity management solutions. The conventional ton-per-kWh allocation approach, often manually adjusted based on historical production and planning data, lacks a scientific basis and fails to accurately identify efficiency differences or assess energy-saving potential, making it difficult to convince participating units. To address this, we propose a dynamic spatiotemporal allocation scheme and develop a multi-objective optimization model that integrates electricity efficiency, operational stability, and production priority. The model incorporates nonlinear efficiency terms, stability components, and priority-weighted items, with constraints including total balance, monthly adjustment limits, and key area protection. Central to the efficiency term is the accurate prediction of liquid production from electricity consumption. We decompose electricity use into three components—core production electricity, auxiliary production electricity, and product transportation electricity—and derive their proportional coefficients through regression of historical data, enabling high-precision liquid production prediction via machine learning using the Light Gradient Boosting Machine (LGBM). The resulting constrained optimization problem is solved using the Sequential Least Squares Programming (SLSQP) algorithm. Validation using both simulated data and Daqing Oilfield field data demonstrates that the scheme effectively achieves electricity reduction targets while significantly mitigating associated liquid production loss, reducing it by 18.0% in simulated experiments and 32.5% in field validation compared to the conventional ton-per-kWh method. This offers a scientific and adaptive electricity management framework that supports refined energy control and facilitates the petroleum industry’s green and low-carbon transformation. Full article
(This article belongs to the Special Issue Machine Learning for Planning and Logistics)
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