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Algorithms
Special Issues
Optimizing Logistics Activities: Models and Applications
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Optimizing Logistics Activities: Models and Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 526

Special Issue Editors

Dr. Antonio Maria Coruzzolo

E-Mail Website
Guest Editor
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy
Interests: inventory management; spare parts; supply chain; additive manufacturing; risk assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Lolli

E-Mail Website
Guest Editor
Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia, 41121 Modena, Italy
Interests: multi criteria decision making; inventory management; spare parts; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Algorithms aims to gather high-quality contributions in the field of optimization for logistics activities. Logistics operations, such as order picking, vehicle routing, production planning, and related processes, can become extremely costly if not properly optimized.
In recent years, the complexity of these activities has increased significantly due to the following factors:

  • The growing flow of products in global supply chains.
  • The integration of automation and robotics, requiring real-time decision-making.
  • The rise of mass customization, which demands flexible and adaptive processes.
  • The exponential growth of e-commerce, leading to high-intensity warehouse operations.

These challenges highlight the urgent need for innovative optimization models capable of addressing new, diverse, and increasingly complex logistics problems. This Special Issue seeks papers that present quantitative approaches applied to real-world logistics challenges. Submissions may focus on, but are not limited to, the following:

  • Mathematical optimization models.
  • Heuristic and metaheuristic methods.
  • Simulation-based optimization.
  • Data-driven optimization and AI-based approaches.
  • Hybrid decision-support frameworks integrating multiple methods.

Both original research articles and comprehensive review papers are welcome. Review articles should aim to provide a clear overview of the current state of the art and outline promising directions for future research. By bringing together theoretical advancements and practical applications, this Special Issue intends to bridge the gap between academic research and industrial implementation, contributing to more efficient logistics systems.

We look forward to receiving your contributions.

Dr. Antonio Maria Coruzzolo
Dr. Francesco Lolli
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-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

  • supply chains
  • logistics
  • data-driven optimization
  • simulation-based optimization
  • heuristic and metaheuristic
  • hybrid decision-support

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Published Papers (2 papers)

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Research

28 pages, 1638 KB  
Article
A Self-Deciding Adaptive Digital Twin Framework Using Agentic AI for Fuzzy Multi-Objective Optimization of Food Logistics
by Hamed Nozari and Zornitsa Yordanova
Algorithms 2026, 19(3), 218; https://doi.org/10.3390/a19030218 - 14 Mar 2026
Viewed by 183
Abstract
Due to the perishable nature of products, high uncertainty, and conflicting objectives, food supply chain logistics management requires dynamic and adaptive decision-making frameworks. In this study, an integrated decision-making architecture is presented that integrates a multi-objective fuzzy optimization model into an adaptive digital [...] Read more.
Due to the perishable nature of products, high uncertainty, and conflicting objectives, food supply chain logistics management requires dynamic and adaptive decision-making frameworks. In this study, an integrated decision-making architecture is presented that integrates a multi-objective fuzzy optimization model into an adaptive digital twin along with an agentic AI-based dynamic goal reset mechanism. The main methodological innovation of this study is not in the separate development of each of these components but in their structured integration in the form of a self-regulating decision-making loop in which the priority of goals is dynamically adjusted based on the current state of the system. Computational results based on real and simulated data show that the proposed framework reduces the total logistics cost by about 4–5% and reduces product waste by about 13% while simultaneously improving the service level by about 4%. Resilience analysis shows faster performance recovery in the face of operational disruptions, and scalability results confirm the controlled growth of computational time with increasing problem size. These findings demonstrate the effectiveness of integrating adaptive digital twins and agentic AI in a multi-objective fuzzy optimization environment for intelligent and resilient food logistics management. Full article
(This article belongs to the Special Issue Optimizing Logistics Activities: Models and Applications)
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25 pages, 1774 KB  
Article
An Agentic Digital Twin Framework for Fuzzy Multi-Objective Optimization in Dynamic Humanitarian Logistics
by Zornitsa Yordanova and Hamed Nozari
Algorithms 2026, 19(3), 198; https://doi.org/10.3390/a19030198 - 6 Mar 2026
Viewed by 314
Abstract
Humanitarian logistics faces challenges such as conflicting objectives, severe uncertainty, temporal dynamics, and the need for interpretable decisions. This research presents an integrated decision-making framework that simultaneously considers fuzzy uncertainty, system dynamics, and adaptive decision logic. Operational uncertainties are modeled using triangular fuzzy [...] Read more.
Humanitarian logistics faces challenges such as conflicting objectives, severe uncertainty, temporal dynamics, and the need for interpretable decisions. This research presents an integrated decision-making framework that simultaneously considers fuzzy uncertainty, system dynamics, and adaptive decision logic. Operational uncertainties are modeled using triangular fuzzy numbers and a dynamic representation of the system allows for continuous updating of decisions over time. Computational results based on simulated data show that the proposed framework is capable of generating stable, diverse, and interpretable solutions. An improvement in the average quality of the Pareto front of more than 5% and a reduction in the distance from the reference front of about 30% are observed compared to non-adaptive approaches. Also, stability and dynamic behavior analyses show that the decisions are robust to changing environmental conditions and parameters and have high adaptability. These features make the proposed framework a reliable tool for decision support in relief operations. Full article
(This article belongs to the Special Issue Optimizing Logistics Activities: Models and Applications)
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