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Algorithms
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Heuristic Optimization Algorithms for Logistics
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Heuristic Optimization Algorithms for Logistics

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: 20 May 2025 | Viewed by 6492

Special Issue Editors

Prof. Dr. José Andrés Moreno Pérez

E-Mail Website
Guest Editor
Department of Computer and Systems Engineering, Instituto Universitario de Desarrollo Regional, University of La Laguna, 38200 La Laguna, Spain
Interests: artificial intelligence; heuristics; optimization; logisitics
Dr. Jesica de Armas

E-Mail Website
Guest Editor
Department of Economics and Business, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain
Interests: optimization; metaheuristics; logistics & transportation; machine learning; home health care
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Heuristics, problem-solving techniques, can be used to address the complexity and uncertainty inherent in logistics operations. Heuristic optimization algorithms have gained significant attention due to their ability to efficiently find near-optimal solutions for complex logistics problems. Logistics, encompassing the management of the flow of goods, services, and information, requires robust optimization techniques to enhance efficiency, reduce costs, and improve overall performance. Heuristics offer practical and computationally efficient solutions for tackling intricate logistics challenges. These algorithms leverage simple rules and iterative processes to explore solution spaces effectively, making them well suited for real-world logistics applications.

This Special Issue aims to bring together cutting-edge research and innovative applications of heuristic optimization algorithms in logistics. Topics of interest include algorithmic developments, case studies, and experimental validations that demonstrate the effectiveness of heuristics in optimizing various aspects of logistics, including load packing, route planning, vehicle scheduling, inventory management, and facility location. This includes the decisions that have to be taken in the design and management of an efficient supply chain, from a strategic (long-term), tactical (medium-term), and operational (short-term) point of view, implying a combination of two or more of the above class of problems. The interdisciplinary nature of this Special Issue encourages contributions from researchers and practitioners in computer science, operations research, and logistics, fostering a collaborative environment for advancing the state of the art in heuristic optimization for logistics. Overall, this Special Issue seeks to contribute to the development of practical and scalable solutions that address the evolving challenges in the dynamic and complex field of logistics.

Prof. Dr. José Andrés Moreno Pérez
Dr. Jesica de Armas
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 1600 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

  • heuristics
  • metaheuristics
  • combinatorial optimization
  • optimization algorithms
  • logistics
  • transport
  • supply chain management

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Published Papers (5 papers)

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Research

19 pages, 293 KiB  
Article
Where to Split in Hybrid Genetic Search for the Capacitated Vehicle Routing Problem
by Lars Magnus Hvattum
Algorithms 2025, 18(3), 165; https://doi.org/10.3390/a18030165 - 13 Mar 2025
Viewed by 339
Abstract
One of the best heuristic algorithms for solving the capacitated vehicle routing problem is a hybrid genetic search. A critical component of the search is a splitting procedure, where a solution encoded as a giant tour of nodes is optimally split into vehicle [...] Read more.
One of the best heuristic algorithms for solving the capacitated vehicle routing problem is a hybrid genetic search. A critical component of the search is a splitting procedure, where a solution encoded as a giant tour of nodes is optimally split into vehicle routes using dynamic programming. However, the current state-of-the-art implementation of the splitting procedure assumes that the start of the giant tour is fixed as a part of the encoded solution. This paper examines whether the fixed starting point is a significant drawback. Results indicate that simple adjustments of the starting point for the splitting procedure can improve the performance of the genetic search, as measured by the average primal gaps of the final solutions obtained, by 3.9%. Full article
(This article belongs to the Special Issue Heuristic Optimization Algorithms for Logistics)
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38 pages, 1822 KiB  
Article
A Survey on Variable Neighborhood Search for Sustainable Logistics
by Jesica de Armas and José A. Moreno-Pérez
Algorithms 2025, 18(1), 38; https://doi.org/10.3390/a18010038 - 10 Jan 2025
Cited by 1 | Viewed by 986
Abstract
Sustainable logistics aims to balance economic efficiency, environmental responsibility, and social well-being in supply chain operations. This study explores the use of Variable Neighborhood Search (VNS), a metaheuristic optimization method, in addressing sustainable logistics challenges and provides insights into the potential it has [...] Read more.
Sustainable logistics aims to balance economic efficiency, environmental responsibility, and social well-being in supply chain operations. This study explores the use of Variable Neighborhood Search (VNS), a metaheuristic optimization method, in addressing sustainable logistics challenges and provides insights into the potential it has to support them by delivering efficient solutions that align with global sustainability goals. The review identifies key trends, including a significant increase in research since 2019, with a strong focus on routing, scheduling, and location problems. Hybrid approaches, combining VNS with other methods, and multiobjective optimization to address trade-offs between sustainability goals are prominent. The most frequently applied VNS versions align closely with those commonly used in the broader literature, reflecting similar adoption proportions. In recent years, a noticeable increase in studies incorporating adaptation mechanisms into VNS frameworks has emerged. This trend is largely driven by the growing influence of Artificial Intelligence approaches across numerous fields of science and engineering, highlighting the need for more dynamic and intelligent optimization techniques. However, important research gaps remain. These include limited consideration of uncertainty and dynamic logistics systems, underrepresentation of social sustainability, and a lack of standardized benchmarks for comparing results. Future work should address these challenges and explore emerging applications. Full article
(This article belongs to the Special Issue Heuristic Optimization Algorithms for Logistics)
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16 pages, 942 KiB  
Article
A Simulated Annealing Algorithm for the Generalized Quadratic Assignment Problem
by Alan McKendall and Yugesh Dhungel
Algorithms 2024, 17(12), 540; https://doi.org/10.3390/a17120540 - 28 Nov 2024
Viewed by 848
Abstract
The generalized quadratic assignment problem (GQAP) involves assigning a set of facilities to a set of locations such that the sum of the assignment and transportation costs is minimized. Unlike the traditional one-to-one assignment problem, the GQAP is a many-to-one assignment problem. That [...] Read more.
The generalized quadratic assignment problem (GQAP) involves assigning a set of facilities to a set of locations such that the sum of the assignment and transportation costs is minimized. Unlike the traditional one-to-one assignment problem, the GQAP is a many-to-one assignment problem. That is, multiple facilities can be assigned to each location without exceeding the capacity of the location. This research was motivated by the problem of assigning multiple facilities (e.g., machines or equipment) to locations at manufacturing plants. Another well-known application of the GQAP includes the assignment of facilities (i.e., containers) to locations (i.e., storage areas) in container yards. This paper presents simple but very effective approximation algorithms for solving real-world, large-size GQAP instances quickly without spending a lot of time setting the algorithm parameters, since there are few parameters to set. More specifically, a construction algorithm is used to generate an initial solution for the proposed problem, and the initial solution is improved using a simulated annealing algorithm. The performance of the proposed algorithms is tested with respect to solution quality and computation time on a set of test problems available in the literature. The results show the effectiveness of the proposed algorithms. Full article
(This article belongs to the Special Issue Heuristic Optimization Algorithms for Logistics)
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19 pages, 1342 KiB  
Article
AASA: A Priori Adaptive Splitting Algorithm for the Split Delivery Vehicle Routing Problem
by Nariman Torkzaban, Anousheh Gholami, John S. Baras and Bruce L. Golden
Algorithms 2024, 17(9), 396; https://doi.org/10.3390/a17090396 - 6 Sep 2024
Viewed by 1218
Abstract
The split delivery vehicle routing problem (SDVRP) is a relaxed variant of the capacitated vehicle routing problem (CVRP) where the restriction that each customer is visited precisely once is removed. Compared with CVRP, the SDVRP allows a reduction in the total cost of [...] Read more.
The split delivery vehicle routing problem (SDVRP) is a relaxed variant of the capacitated vehicle routing problem (CVRP) where the restriction that each customer is visited precisely once is removed. Compared with CVRP, the SDVRP allows a reduction in the total cost of the routes traveled by vehicles. The exact methods to solve the SDVRP are computationally expensive. Moreover, the complexity and difficult implementation of the state-of-the-art heuristic approaches hinder their application in real-life scenarios of the SDVRP. In this paper, we propose an easily understandable and effective approach to solve the SDVPR based on an a priori adaptive splitting algorithm (AASA) that improves the existing state of the art on a priori split strategy in terms of both solution accuracy and time complexity. In this approach, the demand of the customers is split into smaller demand values using a splitting rule in advance. Consequently, the original SDVRP instance is converted to a CVRP instance which is solved using an existing CVRP solver. While the proposed a priori splitting rule in the literature is fixed for all customers regardless of their demand and location, we suggest an adaptive splitting rule that takes into account the distance of the customers to the depot and their demand values. Our experiments show that AASA can generate solutions comparable to the state of the art, but much faster. Full article
(This article belongs to the Special Issue Heuristic Optimization Algorithms for Logistics)
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19 pages, 729 KiB  
Article
A Sim-Learnheuristic for the Team Orienteering Problem: Applications to Unmanned Aerial Vehicles
by Mohammad Peyman, Xabier A. Martin, Javier Panadero and Angel A. Juan
Algorithms 2024, 17(5), 200; https://doi.org/10.3390/a17050200 - 8 May 2024
Cited by 2 | Viewed by 1697
Abstract
In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and [...] Read more.
In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem. Full article
(This article belongs to the Special Issue Heuristic Optimization Algorithms for Logistics)
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