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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: 31 October 2026 | Viewed by 2489

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 (4 papers)

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Research

33 pages, 2162 KB  
Article
Hybrid Narwhale Optimization with Super Modified Simplex and Runge–Kutta Enhancements: Benchmark Validation and Application to Fuzzy Aggregate Production Planning
by Pasura Aungkulanon, Anucha Hirunwat, Roberto Montemanni and Pongchanun Luangpaiboon
Algorithms 2026, 19(4), 295; https://doi.org/10.3390/a19040295 - 9 Apr 2026
Viewed by 319
Abstract
Aggregate production planning (APP) helps medium-term production, manpower, inventory, and subcontracting decisions match expected demand. Deterministic planning models are generally ineffective in manufacturing due to demand and operational variability. Fuzzy linear programming (FLP) has been frequently used to describe imprecision using membership functions [...] Read more.
Aggregate production planning (APP) helps medium-term production, manpower, inventory, and subcontracting decisions match expected demand. Deterministic planning models are generally ineffective in manufacturing due to demand and operational variability. Fuzzy linear programming (FLP) has been frequently used to describe imprecision using membership functions and satisfaction levels. Despite its versatility, accurate approaches for solving multi-objective FLP-based APP models become computationally expensive as issue size and complexity increase. Thus, metaheuristic algorithms are widely used, although many still have premature convergence, parameter sensitivity, and restricted scalability. This study investigates the Narwhal Optimization Algorithm (NO) as a population-based metaheuristic framework. It proposes two hybrid variants to improve convergence reliability and constraint-handling capability: NO combined with the Super Modified Simplex Method (SMS) for local refinement and NO integrated with a Runge–Kutta-based optimizer (RK) for search stability. These hybrid techniques are tested for solution quality, convergence behavior, and robustness using eight response-surface benchmark functions and four constrained optimization problems. A real-parameter fuzzy APP problem with three goods and a six-month planning horizon uses the best variations. The Elevator Kinematic Optimization (EKO) algorithm, chosen for its compliance with the same mathematical framework and consistent parameter values, is used to compare the offered solutions fairly and controlled. Fuzzy programming uses a max–min satisfaction framework with linear membership functions from positive and negative ideal solutions. Computational experiments assess solution quality, stability, and efficiency for nominal and ±10% demand disturbances. The hybrid NO variants better resist premature convergence, stabilize solutions, and satisfy users more than the original NO and benchmark approaches. For small and medium-sized organizations in dynamic situations, hybrid narwhal-based optimization appears to be a reliable and scalable decision-support solution for APP problems under uncertainty. Full article
(This article belongs to the Special Issue Optimizing Logistics Activities: Models and Applications)
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18 pages, 1279 KB  
Article
Distributed and Data-Driven Optimization Frameworks for Logistics-Oriented Decision Support Under Partial and Asynchronous Information
by Manuel J. C. S. Reis
Algorithms 2026, 19(4), 246; https://doi.org/10.3390/a19040246 - 24 Mar 2026
Viewed by 245
Abstract
This paper introduces D3O-GT, a distributed optimization framework designed to operate under partial, heterogeneous, and delayed information—conditions commonly encountered in large-scale logistics and networked decision support systems. The proposed approach integrates gradient tracking with delay-aware updates to address the steady-state bias [...] Read more.
This paper introduces D3O-GT, a distributed optimization framework designed to operate under partial, heterogeneous, and delayed information—conditions commonly encountered in large-scale logistics and networked decision support systems. The proposed approach integrates gradient tracking with delay-aware updates to address the steady-state bias and instability that often affect classical distributed gradient methods. We formulate a consensus optimization model that captures decentralized decision variables while preserving global optimality, and we develop an algorithmic structure that balances convergence accuracy, communication efficiency, and robustness to asynchronous updates. Extensive numerical experiments demonstrate that D3O-GT achieves machine precision convergence in synchronous settings and remains stable under bounded communication delays, converging to a small neighborhood of the optimum. In contrast, conventional distributed gradient descent exhibits significant residual error under the same conditions. Scalability analyses further indicate that the proposed method maintains favorable iteration complexity as the number of agents increases. These results position D3O-GT as a practical and scalable solution for distributed decision-making environments, with direct relevance to logistics-oriented applications such as resource allocation, coordination of networked services, and real-time operational planning. Full article
(This article belongs to the Special Issue Optimizing Logistics Activities: Models and Applications)
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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
Cited by 2 | Viewed by 834
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
Cited by 1 | Viewed by 682
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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